neotoma Documentation€¦ · CHAPTER 1 Acknowledgements This documentation would not be possible without the extrordinary work of Dr. Eric C. Grimm who has spent countless hours

neotoma DocumentationRelease 1.0

Eric Grimm, Simon Goring

May 10, 2016

Contents

1 Acknowledgements 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Database Design Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61.3 SQL Quickly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151.4 Neotoma Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161.5 Chronology & Age Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181.6 Dataset & Collection Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291.7 Sample Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391.8 Site Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471.9 Taxonomy Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 531.10 Publication Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661.11 Contact and Individual Related Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 831.12 References Cited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

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CHAPTER 1

Acknowledgements

This documentation would not be possible without the extrordinary work of Dr. Eric C. Grimm who has spent countlesshours developing this manual. In addition, Neotoma rests on the work of a number of researchers who contributedto the original North American Pollen Database, and subsequent data contributors, including FAUNMAP contributorsand the data contributions of Allan Ashworth. The Neotoma Database would not exist were it not for the ongoingcontributions of authors, data analysts and funding agencies, in particular the National Sciences Foundation. Thismanual draws heavily from Eric Grimm’s original Neotoma manual (v2), published as:

Grimm, E.C., 2008. Neotoma: an ecosystem database for the Pliocene, Pleistocene, and Holocene. Illinois StateMuseum Scientific Papers E Series, 1.

The SQL Server snapshot is accessible from here: http://www.neotomadb.org/snapshots

1.1 Introduction

Neotoma is a public database containing fossil data from the Holocene, Pleistocene, and Pliocene, or approximatelythe last 5.3 million years. The database stores associated physical data from fossil bearing deposits, for examplesediment loss-on-ignition and geochemical data. The database also stores data from modern samples that are used tointerpret fossil data.

The initial development of Neotoma was funded by a grant from the U.S. National Science Foundation Geoinformaticsprogram. This grant is collaborative between Penn State University 1 and the Illinois State Museum 2. It has fivePrinciple Investigators, Russell W. Graham, Eric C. Grimm, Stephen T. Jackson, Allan C. Ashworth, and John W.(Jack) Williams. The database is served from the Center for Environmental Informatics at Penn State University.

Initially, data are being merged from four existing databases: the Global Pollen Database, FAUNMAP (a database ofmammalian fauna), the North American Plant Macrofossil Database, and a fossil beetle database assembled by AllanAshworth. The design of this database is such that many other kinds of fossil data can easily be incorporated in thefuture, for example, the addition of ostracode, diatom, chironmid, and freshwater mussel datasets.

The existing databases were developed in the 1990’s and have not been updated structurally since. New data have beenadded, but the structures of these databases have not changed, despite significant advances in database and internettechnology. Although structurally different, these databases contain similar kinds of data, and merging them was quitepractical. The rationale for this merging was twofold: (1) to facilitate analyses of past biotic communities at theecosystem level and (2) to reduce the overhead in maintaining and distributing several independent databases..

The new Neotoma database was initially designed by E. C. Grimm and implemented in Microsoft® Access®. Thisdatabase will be ported to a higher end RDBMS for Internet distribution, but it will continue to be distributed as astandalone Access database for researchers who need access to the entire database.

1 Grant number 06223492 Grant number 0622289

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http://www.neotomadb.org/snapshots

neotoma Documentation, Release 1.0

1.1.1 Whence Neotoma

In the original NSF proposal, this database was called a “Late Neogene Terrestrial Ecosystem Database.” At the timethis proposal was written, the Neogene Period included the Miocene, Pliocene, Pleistocene, and Holocene epochs.However, a proposal before the International Commission on Stratigraphy would elevate the Quaternary to a Systemor Period following the Neogene and terminate the Neogene at the end of Pliocene.

Because this proposal renders the Neogene description of this database obsolete, a new name was sought. Numerousnames and companion acronyms were considered, but none engendered enthusiastic support. B. Brandon Curry pro-posed the name Neotoma, and this name struck a fancy. Neotoma is the genus for the packrat. Packrats are prodigiouscollectors of anything in their territory, and moreover they are collectors of fossil data. They collect plant macrofossilsand bones, and pollen is preserved in their amberat—hardened, dried urine, which impregnates their middens andpreserves them for millennia.

1.1.2 Rationale

Paleobiological data from the recent geological past have been invaluable for understanding ecological dynamics attimescales inaccessible to direct observation, including ecosystem evolution, contemporary patterns of biodiversity,principles of ecosystem organization, particularly the individualistic response of species to environmental gradients,and the biotic response to climatic change, both gradual and abrupt. Understanding the dynamics of ecological systemsrequires ecological time series, but many ecological processes operate too slowly to be amenable to experimentationor direct observation. In addition to having ecological significance, fossil data have tremendous importance for cli-matology and global change research. Fossil floral and faunal data are crucial for climate-model verification and areessential for elucidating climate-vegetation interactions that may partly control climate.

Basic paleobiological research is site based, and paleobiologists have devoted innumerable hours to identifying, count-ing, and cataloging fossils from cores, sections, and excavations. These data are typically published in papers describ-ing single sites or small numbers of sites. Often, the data are published graphically, as in a pollen diagram, and theactual data reside on the investigator’s computer or in a file cabinet. These basic data are similar to museum collec-tions, costly to replace, sometimes irreplaceable, and their value does not diminish with time. Also similar to museumcollections, the data require cataloging and curation. Whereas physical specimens of large fossils, such as animalbones, are typically accessioned into museums, microfossils, such as pollen, are not accessioned, and the digital dataare the primary objects, and their loss is equivalent to losing valuable museum specimens. The integrated databasethat we propose ensures safe, long-term archiving of these data.

Large independent databases exist for fossil pollen, plant macrofossils, and mammals: the Global Pollen Database(GPD), the North American Plant Macrofossil Database (NAPMD), and FAUNMAP. In addition, a database of fossilbeetles (BEETLE) has been assembled, but it is not yet publicly available. These databases have become essentialcyberinfrastructure. Nevertheless, they were developed as standalone databases in the early 1990’s with PC databasesoftware. GPD and NAPMD are in Paradox®; FAUNMAP is in Access. Since initial database development, emphasishas been placed on ingest of new and legacy data. However, database and Internet technology have advanced greatlyin the past 15 years, and the current relational database software, ingest programs, data retrieval algorithms, outputformats, and analysis tools are outdated and minimal. Moreover, the databases are not linked, so that integratedanalyses are difficult.

Although GPD, NAPMD, and FAUNMAP were developed independently, they have much in common. The basic dataof all three databases as well as BEETLE are essentially lists of taxa from cores, excavations, or sections, often withquantitative measures of abundance. The three databases include similar metadata. The objective of Neotoma is tobuild a unified data structure that will incorporate all of these databases. The database will initially incorporate pollen,plant macrofossil, mammal, and beetle data. However, the database designed facilitates the incorporation of all kindsof fossil data.

Various teams of investigators have developed databases for paleobiological data that have been project or disciplinebased, including the four databases to be integrated in this project. However, long-term maintenance and sustainabilityhave been problematic because of the need to secure continuous funding. Nevertheless, these databases have become

2 Chapter 1. Acknowledgements


the established archives for their disciplines and, new data are continuously contributed. However, because of fundinghiatuses, long spells may intervene between times of data contribution and their public availability. For example, theplant macrofossil database has not incorporated any new data since 1999. The number of different databases anddisciplines exacerbates the problem, because each database requires a database manager. Consolidation of informaticstechnology helps address this overhead issue. However, specialists are still essential for management and supervisionof data collection and quality control for their disciplines or organismal groups.

The purposes of Neotoma are (1) to facilitate studies of ecosystem development and response to climate change, (2) toprovide the historical context for understanding biodiversity dynamics, including genetic diversity, (3) to provide thedata for climate-model validation, (4) to provide a safe, long-term, low-cost archive for a wide variety of paleobiolog-ical data. Site-based studies are invaluable in their own right, and they are the generators of new data. However, muchis gained by marshalling data from geographic arrays of sites for synoptic, broad-scale ecosystem studies. In order tocarry out such studies efficiently, a queryable database is required. Thus, it is much more than an archive; it is essentialcyberinfrastructure for paleoenvironmental research. The database facilitates integration, synthesis, and understand-ing, and it promotes information sharing and collaboration. The individual databases have been extensively used forscientific research, with several hundred scientific publications directly based upon data drawn from these databases.This project will enhance those databases and will continue their public access. By integrating these databases andby simplifying the contributor interface, we can reduce the number of people necessary for community-wide databasemaintenance, and thereby help ensure their long-term sustainability and existence.

History of the Constituent Databases

Global Pollen Database

In an early effort, the Cooperative Holocene Mapping Project (COHMAP Members 1988, Wright et al. 1993) assem-bled pollen data in the 1970s and 1980s to test climate models. Although data-model comparison was the principalobjective of the COHMAP project, the synoptic analyses of the pollen data, particularly maps showing the constantlyshifting ranges of species in response to climate change, were revelatory and led to much ecological insight (e.g. Webb1981, 1987, 1988).

The COHMAP pollen “database” consisted of a multiplicity of flat files with prescribed formats for data and chronolo-gies. FORTRAN programs were written to read these files and to assemble data for particular analyses. ThompsonWebb III managed the COHMAP pollen database at , but as the quantity of data increased, data management be-came increasingly cumbersome. Clearly, the data needed to be migrated to a relational database management system.Discussions with E. C. Grimm led to the initiation of the North American Pollen Database (NAPD) at the in 1990.

At the same time in , the International Geological Correlation Project IGCP 158 was conducting a major collaborativesynthesis of paleoecological data, primarily of pollen, and the need for a pollen database became painfully obvious. Inthe forward to the book resulting from this project (Berglund et al. 1996), J.L. de Beaulieu describes the role that thisproject had in launching the European Pollen Database. A workshop to develop a European Pollen Database (EPD)was held in in 1989. North American representatives also attended, and the organizers of NAPD and EPD commenceda long-standing collaboration to develop compatible databases. NAPD and EPD held several joint workshops anddeveloped the same data structure. Nevertheless, the two databases were independently established, partly becauseInternet capabilities were not yet sufficient to easily manage a merged database. The pollen databases were developedin Paradox, which at the time was the most powerful RDBMS readily available for the PC platform. NAPD and EPDestablished two important protocols:

1. the databases were relational and queryable

2. they were publicly available.

As the success the NAPD-EPD partnership escalated, working groups initiated pollen databases for other regions,including the Latin American Pollen Database (LAPD) in 1994, the Pollen Database for and the Russian Far East(PDSRFE) in 1995, and the African Pollen Database (APD) in 1996. At its initial organizational workshop, LAPDopted to merge with NAPD, rather than develop a standalone database, and the Global Pollen Database was born.PDSRFE also followed this model. APD developed independently, but uses the exact table structure of GPD and EPD.

1.1. Introduction 3


Pollen database projects have also been initiated in other regions, and the GPD contains some of these data, includingthe Indo-Pacific Pollen Database and the Japanese Pollen Database.

The pollen databases contain data from the Holocene, Pleistocene, and Pliocene, although most data are from thelast 20,000 years. Included are fossil data, mainly from cores and sections, and modern surface samples, which areessential for calibrating fossil data. NAPD data are not separate from the GPD, but rather NAPD is the North Americansubset of GPD. EPD has both public and restricted data—a concession that had to be made early on to assuage somecontributors.

North American Plant Macrofossil Database

Plant macrofossils include plant organs generally visible to the naked eye, including seeds, fruits, leaves, needles,wood, bud scales, and megaspores. Synoptic-scale mapping of plant macrofossils from modern assemblages (Jacksonet al. 1997) and fossil assemblages (Jackson et al. 1997, Jackson et al. 2000, Jackson and Booth 2002) have shown theutility of plant macrofossils in providing spatially and taxonomically precise reconstructions of past species ranges.Although plant macrofossil records are spatially precise, synoptic networks of high-quality sites can scale up to yieldaggregate views of past distributions (Jackson et al. 1997). In addition, macrofossils, with their greater taxonomicresolution, augment the pollen data by providing information on which species might have been present, and canresolve issues of long-distance transport (Birks 2003).

The North American Plant Macrofossil Database (NAPMD) has been directed by S.T. Jackson at the . Highest priorityhas been placed on data from the last 30,000 years, although some earlier Pleistocene and late Pliocene data areincluded. The database originated as a research database for selected taxa from Late Quaternary sediments of easternNorth America (Jackson et al. 1997). In 1994, an effort was initiated with NOAA funding to build on this foundationto develop a cooperative, relational database comprising all of , a longer time span, and all plant taxa.

The structure of NAPMD was adapted from the pollen database and is also in Paradox. The principal modificationsmade to the pollen database structure to accommodate plant macrofossils were those to cope with different organsfrom the same species and to deal with the various quantitative measures of abundance. The database also includessurface samples, which are useful for interpretation of fossil data.

FAUNMAP

R.W. Graham, E.L. Lundelius, Jr., and a group of Regional Collaborators organized a project to develop a database forlate Quaternary faunal data from the , which the U.S. NSF funded in 1990. This project had a research agenda, andits seminal paper focused on the individualistic behavior displayed by animal species (FAUNMAP Working Group1996).

Two FAUNMAP databases exist, FAUNMAP I and FAUNMAP II. Both databases were coordinated by R. W. Grahamand E. L. Lundelius, Jr. and funded by NSF. Both are relational databases for fossil mammal sites. The data wereextracted from peer-reviewed literature, selected theses and dissertations, and selected contract reports for both pale-ontology and archaeology. Unpublished collections were not included. Data were originally captured in Paradox butwere later migrated to Access.

FAUNMAP I contains data from sites in the lower 48 states that date between 500 BP and ~40,000 BP. Funding forthis project ended in 1994, with the production of two major publications by the FAUNMAP Working Group (1994,1996), as well as numerous other publications by individual members and by many others who accessed the databaseon-line. Graham and Lundelius continued the FAUNMAP project, developing FAUNMAP II with funding from NSFbeginning in 1998. FAUNMAP II shares the same structure as FAUNMAP I but expands the spatial coverage toinclude and and extends the temporal coverage to the Pliocene (5 Ma). In addition, sites published since 1994, whenFAUNMAP I was completed, have been added for the contiguous 48 states. In all, FAUNMAP I and II contain morethan 5000 fossil-mammal sites with more than 600 mammal species for all of North America north of Mexico thatrange in age from 0.5 ka to 5 Ma.



The detailed structure of the FAUNMAP database is described in FAUNMAP Working Group (1994). Sites identifiedby name and location were subdivided into Analysis Units (AU’s), which varied from site to site depending upon thedefinitions used in the original publications (e.g., stratigraphic horizons, cultural horizons, excavation levels, bios-tratigraphic zones). All data (i.e. taxa identified and counts of individual specimens) and metadata (sediment types,depositional environments, facies, radiometric and other geochronological dates, modifications of bone) were capturedby AU. This structure allows for the extraction of information at either the level of the site or the smallest subdivision(AU). The AU permits fine-scale temporal resolution and analysis. Similar to GPD and NAPMD, FAUNMAP containsarchival and research tables. Similar to the plant macrofossil database, FAUNMAP contains a variety of quantitativemeasures of abundance, and presence data are more commonly used for analysis.

BEETLE

Many beetles have highly specific ecological and climatic requirements and are valuable indicators of past environ-ments (Morgan et al. 1983, Ashworth 2001, 2004). They are one of the most diverse groups of organisms on earth,and of the insects, perhaps the most commonly preserved as fossils. Allan Ashworth has assembled a database offossil beetles from . The data, which were recorded in Excel, contain 5523 individual records of 2567 taxa from 199sites and 165 publications. Metadata include site name, latitude and longitude, lithology of sediment, absolute age,and geological age. The basic data are similar to plant and mammal databases—lists of taxa from sites. The metadatahave not been recorded to the extent of the other databases, especially chronological data, but Ashworth has resolvedthe taxonomic issues and has assembled the publications, so that the additional metadata can be easily pulled together.

1.1.3 Who Will Use Neotoma?

The existing databases have been used widely for a variety of studies. Because the databases have been availableon-line, precise determination of how many publications have made use of them is difficult. In addition, the databasesare widely used for instructional purposes. Below are examples of the kinds of people who have used these databasesand who we expect will find the new, integrated database even more useful.

• Paleoecologists seeking to place a new record into a regional/continental/global context (e.g., Bell and Mead1998, Czaplewski et al. 1999, Bell and Barnosky 2000, Newby et al. 2000, Futyma and Miller 2001, Gavin etal. 2001, Czaplewski et al. 2002, Schauffler and Jacobson 2002, Camill et al. 2003, Rosenberg et al. 2003,Willard et al. 2003, Pasenko and Schubert 2004, and many others).

• Synoptic paleoecologists interested in mapping regional to sub-continental to global patterns of vegetationchange (e.g., Jackson et al. 1997, Williams et al. 1998, Jackson et al. 2000, Prentice et al. 2000, Thompson andAnderson 2000, Williams et al. 2000, Williams et al. 2001, Williams 2003, Webb et al. 2004, Williams et al.2004, Asselin and Payette 2005).

• Synoptic paleoclimatologists building benchmark paleoclimatic reconstructions for GCM evaluation (e.g.,Bartlein et al. 1998, Farrera et al. 1999, Guiot et al. 1999, Kohfeld and Harrison 2000, CAPE Project Members2001, Kageyama et al. 2001, Kaplan et al. 2003).

• Paleontologists trying to understand the timing, patterns, and causes of extinction events (e.g., Jackson andWeng 1999, Graham 2001, Barnosky et al. 2004, Martínez-Meyer et al. 2004, Wroe et al. 2004).

• Evolutionary biologists mapping the genetic legacies of Quaternary climatic variations (e.g., Petit et al. 1997,Fedorov 1999, Tremblay and Schoen 1999, Hewitt 2000, Comps et al. 2001, Good and Sullivan 2001, Petit etal. 2002, Kropf et al. 2003, Lessa et al. 2003, Petit et al. 2003, Hewitt 2004, Lascoux et al. 2004, Petit et al.2004, Whorley et al. 2004, Runck and Cook 2005).

• Macroecologists interested in temporal records of species turnover and biodiversity and historical controls onmodern patterns of floristic diversity (e.g., Silvertown 1985, Qian and Ricklefs 2000, Brown et al. 2001, Haskell2001).

• Archeologists who are studying human subsistence patterns and interactions with their environment (e.g.,Grayson 2001, Grayson and Meltzer 2002, Cannon and Meltzer 2004, Grayson in press).

1.1. Introduction 5


• Natural resource managers who need to know historical ranges and abundances of plants and animals fordesigning conservation and management plans (e.g., Graham and Graham 1994, Cole et al. 1998, Noss et al.2000, Owen et al. 2000, Committee on Ungulate Management in Yellowstone National Park 2002, Burns et al.2003)

• Scientists trying to understand the potential response of plants, animals, biomes, ecosystems, and biodiversityto global warming (e.g., Bartlein et al. 1997, Davis et al. 2000, Barnosky et al. 2003, Burns et al. 2003, Kaplanet al. 2003, Schmitz et al. 2003, Jackson and Williams 2004, Martínez-Meyer et al. 2004)

• Teachers who use the databases for teaching purposes and class exercises.

1.2 Database Design Concepts

1.2.1 Sites, Collection Units, Analysis Units, Samples, and Datasets

Fossil data are site based. A Site has a name, latitude-longitude coordinates, altitude, and areal extent. In Neotoma,Sites are designated geographically as boxes with north and south latitude coordinates and east and west longitudecoordinates. If the areal extent is not known, the box collapses to a point, with the north and south latitudes equaland the east and west longitudes equal. Most of the legacy sites in Neotoma currently have point coordinates. Thelat-long box can circumscribe the site, for example a lake, or it may circumscribe a larger area in which the site lieseither because the exact location of the site is not known or because the exact location is purposely kept vague. In thecase of many legacy sites, the exact location is not know precisely; for example, it may have been described as «on agravel bar 5 miles east of town». The exact locations of some sites have purposely been kept vague to prevent lootingand vandalism.

A Collection Unit is a unit from a site from which a collection of fossils or other data have been made. TypicalCollection Units are cores, sections, and excavation units. A site may have several Collection Units. A CollectionUnit is located spatially within a site and may have precise GPS latitude-longitude coordinates. Its definition is quiteflexible. For pollen data, a Collection Unit is typically a core, a section, or surface sample. A Collection Unit canalso be a composite core comprised of two or more adjacent cores pieced together to form a continuous stratigraphicsequence. A Collection Unit can also be an excavation unit. For faunal data, a Collection Unit could be as precise asan excavation square, or it could be a group of squares from a particular feature within a site. For example, considera pit cave with three sediment cones, each with several excavation squares. Collection Units could be defined as theindividual squares, or as three composite Collection Units, one from each sediment cone. Another example is anarchaeological site, from which the reported Collection Units are different structures, although each structure mayhave had several excavation squares. The precision in the database depends on how data were entered or reported.

For many published sites, the data are reported from composite Collection Units. If faunal data are reported froma site or locality without explicit Collection Units, then data are assigned to a single Collection Unit with the name«Locality». This is a «quote».

Different kinds of data may have been collected from a single Collection Unit, for example fauna and macrobotanicalsfrom an excavation, or pollen and plant macrofossils from a lake-sediment core. A composite Collection Unit mayinclude data from different milieus, which, nevertheless, are associated with each other, for example a diatom samplefrom surficial lake sediments and an associated lake-water sample for water-chemistry measurements.

The Collection Unit is equivalent to the Entity in the Global Pollen Database but was not defined in FAUNMAP. Whenthe FAUNMAP data were imported into Neotoma, most localities were assigned a single «Locality» Collection Unit.However, for some localities, the data were assigned to different Collection Units that were clearly identifiable inFAUNMAP (see Figure 1).

An Analysis Unit is a stratigraphic unit within a Collection Unit and is typically defined in the vertical dimension. AnAnalysis Unit may be a natural stratigraphic unit with perhaps irregular depth and thickness or it may be an arbitraryunit defined by absolute depth and thickness. An excavation may have been dug in arbitrary units, for example 10cm levels, or it may have followed natural stratagraphic boundaries, for example the «red zone» or a feature in an



archaeological site. Although Analysis Units could be designated by an upper depth and lower depth, in Neotomathey are designated by their midpoint depth and thickness, which is more convenient for developing age models.Pollen and other microfossils are typically sampled at arbitrary depths, and although these samples have thicknessescorresponding to the thickness of the sampling device (usually 1 cm or less), these thicknesses are often not reported,just the depths. Different kinds of samples may have been taken from a single analysis unit, for example pollen,diatoms, and ostracodes. The Analysis Unit links these various samples together.

In larger excavations, natural stratigraphic Analysis Units may cut across excavation squares or Collection Units, andthe data are reported by Analysis Unit rather than by Collection Unit. In this case, the fossil data are assigned to ageneric composite Collection Unit named «Locality», which has the explicitly defined Analysis Units. If the AnalysisUnits are not described or reported, then the data are assigned to a single Analysis Unit with the name «Assemblage».Thus, for a locality published with only faunal list, the fauna are assigned to a Collection Unit named «Locality» andto an Analysis Unit named «Assemblage».

In FAUNMAP, Analysis Units are the primary sample units, and fauna are recorded by Analysis Unit. In the GPD,Analysis Units correspond to samples.

Samples are of a single data type from an Analysis Unit. For example, there may be a vertebrate faunal sample and amacrobotanical sample from the same Analysis Unit; or there may be a pollen sample and an ostracode sample fromthe same Analysis Unit. There can be multiple samples of the same data type from an Analysis Unit, for example twopollen samples counted by different analysts. Normally, vertebrate fossils from an Analysis Unit comprise a singlesample; however, if the fossils are of mixed age, individually dated bones may be treated as separate samples, each witha precise age. In addition to fossils, samples may also be used for physical measurements, such as loss-on-ignition.Geochronologic measurements, such as radiocarbon dates, are made on geochronologic samples.

A Dataset is a set of Samples of a single data type from a Collection Unit. For example the pollen data from a corecomprise a pollen Dataset. The geochronologic samples from a Collection Unit form a geochronologic Dataset. EverySample is assigned to a Dataset, and every Dataset is assigned to a Collection Unit. Samples from different CollectionUnits cannot be assigned to the same Dataset (although they may be assigned to Aggregate Datasets).

Figure 1. Diagram showing the relationships between tables in Neotoma, the Pollen Database, and FAUNMAP.Because the pollen database has only pollen, no need exists for Analysis Units, which may have multiple data types.FAUNMAP does not make a hierarchical distinction between Collection Units and Analysis Units, and the data forboth Analysis Units and fauna are contained in the Faunal table, although within the Faunal table, implicit one-to-manyrelationships exist between Localities and Analysis Units and between Analysis Units and faunal data.

1.2.2 Taxa and Variables

In general, a sample in Neotoma has a list of taxa with some measure of abundances. The Data table in Neotoma hasfields for SampleID, VariableID, and Value. Variables, which are listed in the Variables table, consist of a Taxon,referenced in the Taxa table, as well as the identified Element, measurement Units, Context, and Modification. ATaxon is generally a biological Taxon, but a Taxon may also be a physical attribute such as loss-on-ignition.

For biological taxa, the Element is the organ or skeletal element. Typical faunal Elements are bones, teeth, scales, andother hard body parts. Bone and tooth Elements may be specifically identified (e.g. «tibia» or even more precisely«tibia, distal, left», «M2, lower, left»). Some soft Elements also occur in the database (e.g. «hair» and «dung»).For mammals, an unspecified element is «bone/tooth». Elements for plant macrofossils are the organs identified (e.g.

1.2. Database Design Concepts 7


«seed», «needle», «cone bract»). Pollen and spores are treated simply as taxon Elements. Thus, Picea seeds, Piceaneedles, and Picea pollen are three different Variables. All three refer to a single entry in the Taxa table for Picea.

• Variable Units are the measurement units. For faunal data, the most common are «present/absent», «numberof individual specimens» (NISP), and «minimum number of individuals» (MNI). Plant macrofossils have manydifferent quantitative and semi-quantitative measurement Units, including concentrations and relative abundancescales. Measurement Units for pollen are NISP (counts) and «percent». For pollen the preferred measurementUnit is NISP, but for some sites only percentage data are available. Picea pollen NISP and Picea pollen percentare two different Variables.

• Variable Contexts for fauna include «articulated», «intrusive», and «redeposited». A context for pollen is«anachronic», which refers to a pollen type known to be too old for the contemporary sedimentary deposit.Most Variables do not have a specified context.

• Variable Modifications include various modifications to fossils or modifiers to Variables, including human mod-ifications to bones (e.g. «bone tool», «human butchering», «burned») and preservational and taphonomic modi-fications (e.g. «carnivore gnawed», «fragment»). Modifications for pollen include preservational classificationssuch as «corroded» and «degraded».

1.2.3 Taxonomy and Synonymy

Neotoma does not change or question identifications from original sources, although taxonomic names may be syn-onymized to currently accepted names. Thus, for example, the old (although still valid) non-standard plant familynames such as Gramineae and Compositae are synonimized to their standard family names terminated with «-aceae»,viz. Poaceae and Asteraceae. Neotoma has not attempted to establish complete or comprehensive synonymies. How-ever, the Synonyms table lists commonly encountered synonyms. The descriptions of the SynonymTypes and Taxatables contain fuller discussions of synonymiztions made in Neotoma.

An important feature of Neotoma is that the Taxa table is hierarchical. Each Taxon has a HigherTaxonID, which is theTaxonID of the next higher taxonomic rank. Thus, data are stored at the highest taxonomic resolution reported by theoriginal investigators, but can be extracted at a higher taxonomic level.

Synonymy presents a challenge for any organismal database, particularly for one such as Neotoma, which archivesdata collected for over a century and which archives extinct taxa, often for which few and fragmentary specimensexist. Many changes are due to increased understanding of the diversity within taxonomic groups and of the phylo-genetic relationships within and among groups. Other changes are due purely to taxonomic rules or conventions setby the International Code of Botanical Nomenclature (McNeill et al. 2006) and the International Code of Zoologi-cal Nomenclature (International Commission on Zoological Nomenclature 1999). Working groups representing thedifferent taxonomic groups included in Neotoma have established appropriate taxonomic authorities:

• Plants – There is no worldwide authority. The International Plant Names Index 3 lists validly published names,but a listed name is not necessarily the accepted name for a given taxon. For families, Neotoma follows theAngiosperm Phylogeny Group II (2003) and Stevens (2007+), which follows and updates APG II. The APGis an international consortium of plant taxonomists, and the APG classification utilizes the great quantity ofphylogenetic data generated in recent years. For lower taxonomic ranks, the various pollen database cooperativesfollow appropriate regional floras:

• North American Pollen Database/North American Plant Macrofossil Database: Insofar as possible, follows theFlora of North America (Flora of North America Editorial Committee 1993+); about half of the planned FNAvolumes have been published. Otherwise, appropriate regional floras are followed.

• European Pollen Database: The EPD has a Taxonomy Support Group. In general, nomenclature follows FloraEuropaea (Tutin 1964-1993).

3 http://www.ipni.org


http://www.ipni.org


• African Pollen Database: The APD has a Committee for Nomenclature, which has produced a list of pollentypes with misspellings, synonymy, and nomenclature corrected 4. APD nomenclature follows Enumération desplantes à fleurs d’Afrique Tropicale (Lebrun and Stork 1991-1997).

• Latin American Pollen Database: has a tremendously rich and diverse flora and no comprensive flora is available.Various regional floras are followed.

• Indo-Pacific Pollen Database: For Australia and adjacent areas follows the Australian Plant Name Index (Chap-man 1991). For other regions, appropriate regional floras are followed.

• Pollen Database for Siberia and the Russian Far East Follows Vascular Plants of Russia and Adjacent States(Czerepanov 1995).

• Mammals – For extant taxa, the authority is Wilson and Reeder’s (2005) Mammal Species of the World . Originalsources are followed for extinct species, and the database is considered authoritative.

• Birds – For North America, the authority is the American Ornithologists’ Union Check-list of North AmericanBirds (American Ornithologists’ Union 1983).

• Fish – Follows the Catalog of Fishes (Eschmeyer 1998).

• Mollusks – For North America, follows Common and Scientific Names of Aquatic Invertebrates from the UnitedStates and Canada: Mollusks (Turgeon et al. 1998).

• Beetles – Comprehensive manuals do not exist. Original taxonomic authorities are cited, and the database isconsidered authoritative.

1.2.4 Taxa and Ecological Groups

In the Taxa table, each taxon is assigned a TaxaGroupID, which refers to the TaxaGroupTypes table. These aremajor taxonomic groups, such as «Vascular plants», «Diatoms», «Testate amoebae», «Mammals», «Reptiles andamphibians», «Fish», and «Molluscs». Also included are «Charcoal» and «Physical variables». Ecological Groupsare groupings of taxa within Taxa Groups, which may be ecological or taxonomic. Ecological Groups are assigned inthe EcolGroups table, in which taxa are assigned an EcolGroupID, which links to the EcolGroupTypes table, and anEcolSetID, which links to the EcolSetTypes table. Ecological Groups are commonly used to organize taxa lists anddiagrams. For any taxonomic group, more than one Ecological Set may be assigned. For example, beetles may beassigned to a set of ecological groups, such as dung and bark beetles, and to second set based on taxonomy. Vascularplants are assigned to a «Default plant» set comprised of groups such as «Trees and Shrubs», «Upland Herbs», and«Terrestrial Vascular Cryptogams». Default pollen diagrams can then be generated based on a pollen sum of thesethree groups. Mammals are assigned to a «Vertebrate orders» set.

1.2.5 Chronology

Neotoma stores both the archival data used to reconstruct chronologies as well as interpreted chronologies derivedfrom the archival data. The basic data used to reconstruct chronologies occurs in three tables: Geochronology,Tephrachronology, and RelativeChronology. The Geochronology table includes geophysical measurements such asradiocarbon, thermoluminescence, uranium series, and potassium-argon dates. This table also includes dendrochrono-logical dates derived from tree-ring chronologies, for example logs in archaeological structures. The Tephrachronologytable records tephras in Analysis Units. This table refers to the Tephras lookup table, which stores the ages for knowntephras. The RelativeChronology table stores relative age information for Analysis Units. Relative age scales includethe archaeological time scale, geologic time scale, geomagnetic polarity time scale, marine isotope stages, NorthAmerican land mammal ages, and Quaternary event classification. For example, diagnostic artifacts from an archae-ological site may have cultural associations with a known age ranges, which can be assigned to Analysis Units. Thefaunal assemblage from an Analysis Unit may be assignable to particular land mammal age, which places it withina broad time range. Sedimentary units may be assigned to particular geomagnetic chrons, marine isotope stages, or

4 http://medias.obs-mip.fr/apd/


http://medias.obs-mip.fr/apd/


Quaternary events, such as a particular interglacial. Many of these relative ages have rather broad time spans, but doprovide some chronologic control.

Actual Chronologies are constructed from the basic chronologic data in the Geochronology, Tephrachronology, andRelativeChronology tables. These chronologies are stored in the Chronologies table. A Chronology applies to aCollection Unit and consists of a number of Chron Controls, which are ages assigned to Analysis Units. A ChronControl may be an actual geochronologic measurement, such as a radiocarbon date, or it may be derived from theactual measurement, such as a radiocarbon date adjusted for an old carbon reservoir or calibrated to calendar years. AChron Control may by an average of several radiocarbon dates from the same Analyis Unit. Different kinds of basicchronologic data may be used to assign an age to an Analysis Unit, for example radiocarbon dates and diagnosticarchaeological artifacts. Some relative Chron Controls are not from one of the established relative time scales. Exam-ples of these are local biostratigraphic controls, which may be based on dated horizons from nearby sites. A familiarexample in is the Ambrosia-rise, which marks European settlement. The exact date varies regionally, depending onwhen settlement occurred locally. For a given site, the date assigned to the Ambrosia-rise may be based on historicalinformation about when settlement occurred or possibly on geophysical dating (e.g. 210Pb) of a nearby site.

Forcontinuous stratigraphic sequences, such as cores, not every Analysis Unit may have a direct date. Therefore, agesare commonly interpolated between dated Analysis Units. In this case, the ChronControls are the age-depth controlpoints for an age model, which may be linear interpolation between Chron Controls or a fitted curve or spline.



Figure 2. Smoothed quick radiocarbon calibration curve. At the scale of this figure the difference is mostly lessthan the line thickness.

Age is measured in different time scales, the two most commn being radiocarbon years before present (14C yr BP) orpresumed calendar years before present (cal yr BP). For a calibrated radiocarbon date, «cal yr BP» technically standsfor «calibrated years before present», i.e. calibrated to calendar years. In Neotoma, «cal yr BP» is used for bothcalibrated radiocarbon years and for other ages scales presumed to be in calendar years, viz. dendrochronologic yearsand other geochronlogic ages believed to be in calendar years. The zero datum for any «BP» age is ad 1950, regardlessof its derivation. Thus, BP ages younger than ad 1950 are negative—ad 2000 = 50 BP.

Agesmay be reported in ad/bc age units, in which case bc years are stored as negative values. If ages are reported witha datum other than ad 1950 for BP years, the ages must be converted to an ad 1950 datum or to the ad/bc age scalebefore entry into Neotoma. For example, 210Pb dates are often reported relative to the year of analysis; these must beconverted to either ad/bc or «cal yr BP» with an ad 1950 datum.

Figure 3. An enlarged portion of Figure 2 showing the monontonic smoothed curve



Radiocarbon years can be calibrated to calendar years with a calibration curve. The current calibration curve for26,000 cal yr BP (=21,341 14C yr BP) is the INTCAL04 calibration curve (Reimer et al. 2004). Various programs,both online and standalone, are available for calibrating individual radiocarbon dates, two of the more popular areCALIB 5 (Stuiver and Reimer 1993) and OxCal 6 (Bronk Ramsey 1995, 2001), both available online for download.Calibration of radiocarbon years beyond the INTCAL04 curve is more controversial. However, the Fairbanks0107curve is available for calibration of radiocarbon dates to 50,000 cal yr BP, the practial limit of radiocarabon dating(Fairbanks et al. 2005, Chiu et al. 2007), with an online application 7.

Figure 4. Sample ages calculated from the Neotoma quick calibraton curve vs. ages calculated from traditional agemodels.

Calibrated radiocarbon dates better represent the true time scale and the true errors and probability distributions of theage estimates. In addition, other important paleo records, notably the ice cores and tree-ring records, have calendar-year time-scales. Therefore, for comparison among proxies and records, it is clearly desirable to place all records onthe same time-scale, viz. a calendar-year time-scale. Although this goal is laudable, most of the data ingested intoNeotoma from other databases is on a radiocarbon time scale. The majority of assigned ages and almost all the agesfrom the pollen database are interpolated ages derived from age models. The proper method for deriving calibratedages is to calibrate the radiocarbon dates and then reinterpolate new ages between these calibrated dates.

Virtually all age models are problematic. A key problem is that most age models linearly interpolate between age-

5 http://calib.qub.ac.uk/calib/6 http://c14.arch.ox.ac.uk/embed.php?File=oxcal.html7 http://radiocarbon.ldeo.columbia.edu/research/radcarbcal.htm


http://calib.qub.ac.uk/calib/

http://c14.arch.ox.ac.uk/embed.php?File=oxcal.html

http://radiocarbon.ldeo.columbia.edu/research/radcarbcal.htm


depth points or fit functions or splines to points. However, radiocarbon ages are not points, but probability distributions.Moreover, the probability distributions of calibrated ages are non-Gaussian. Each calibrated age has a unique proba-bility distribution, and many are bimodal or multimodal. Various investigators have used different points, includingthe intercepts of the radiocarbon age with the calibration curve and the midpoint of the 1𝜎 or 2𝜎 probability distribu-ton. The former is particularly inappropriate (Telford et al. 2004b). The 50% median probability is probably the bestsingle point; however, because of multimodality, this particular point may, in fact, be very unlikely. Nevertheless, ifit falls between more-or-less equally probable modes, it may still be the best single point. Most age models for coresare based on relataively few radiocarbon dates, and the uncertainties of the interpolated ages are unknown and large(Telford et al. 2004a). Indeed, chronology is perhaps the greatest challenge for future research with this database.

Figure 5. Anomalies (Sample ages from Neotoma default calendar-year age models minus ages calculated withthe Neotoma quick calibration curve) vs. time.

Given the need for a common age scale and the enormity of the task to properly develop new age models, a Radiocar-bonCalibration conversion table was developed to quickly convert sample ages in radiocarbon years to calendar years.These calibrated ages are for perusal and data exploration; however, the differences between these ages and thosecalculated with traditional age models are relatively small. The table contains radiocarbon ages from -100 to 45,000in 1-year increments with corresponding calibrated values. The table was generated by smoothing the INTCAL04calibration curve with an FFT filter so that the curve is monotonically increasing, i.e. so that there are no age reversalsin calibrated age. The INTCAL04 curve is in 5-yr increments from -5 to 12,500 14C yr BP, 10-yr increments from12,500 to 15,000 14C yr BP, and 20-yr increments from 15,000 to 26,000 14C yr BP. The FFT filter was 50 points (250yr) for the first interval, 25 points (250 yr) for the second interval, and 10 points (200 yr) for the third interval. Forthe calibration beyond 26,000 14C yr BP, a calibrated age was determined with the Fairbanks0107 calibration curveevery 100 years with a standard deviation of ±100 years from 20,000±100 14C yr BP to 46,700±100 14C yr BP. Thesewere then smoothed with a 5-sample (500-yr) FFT filter. The curve kinks sharply after 45,000 14C yr BP, so the quickcalibration curve was terminated at this date. The Fairbanks0107 curve diverges somewhat from the INTCAL04 curvefor the portion they overlap in age. From 20,000 to 26,000 14C yr BP, the difference was prorated linearly from zerodivergence from the INTCAL04 curve at 20,000 14C yr BP to zero divergence from the Fairbanks0107 curve at 26,00014C yr BP. Figure 2 shows the smoothed curve, and Error! Reference source not found. shows an enlargement of partof the curve.

An analysis was made to assess the deviation between ages derived from traditionally calibrated age models and agesderived from the quick calibration curve. From the database, 57 default Chronologies in calibrated radiocarbon yearswere selected. The Chron Controls were all calibrated radiocarbon dates, except for top dates, European settlementdates, and 210Pb dates in the uppermost portions of the cores. A few Chronologies used the Zdanowicz et al. (1999)calendar-year age from the GISP2 ice core. Ages beyond the reliable age limit (Chronologies.AgeBoundOlder) were



not used. These 57 Chronologies had a total of 1945 Sample Ages in calibrated radiocarbon years. Figure 4 showsgraph of ages from the Neotoma age models vs. the ages calculated with the quick calibration curve. Error! Referencesource not found. shows the anomalies vs. time and Figure 6 shows a histogram of the distribution of anomalies.Nearly half (47%) of the anomalies are <25 years, 86% are <100 years, 97% are <200 years, and 99.4% are <300years. The average absolute anomaly is 49.2 years, and the median is 29 years. Thus, the quick calibration curveprovides remarkably good results. The ages have no confidence limits, but neither do the interpolated ages of mostage models.

Figure 6. Binned distribution of anomalies between Neotoma default calendar-year age models and ages calculatedwith the Neotoma quick calibration curve.

1.2.6 Sediment and Depositional Environments

Several tables deal with depositional environments, depositional agents, and sediment descriptions. In Neotoma, theDepositional Environment refers to the Depositional Environment of the site today, for example, «», «Fen», «Cave»,«Colluvial Fan». Depositional Environments may vary within a Site. For example, a lake with a marginal fen haslake and fen Depositional Environments. Thus, Depositional Environments are an attribute of Collection Units and areassigned in the CollectionUnits table. Depositional Environments are listed in the in the DepEnvtTypes lookup table,and they are hierarchical, for example:

Glacial Lacustrine

Any of these Depositional Environments may be assigned to a Collection Unit, but because they are hierarchical,Collection Units may be grouped at higher levels, for example, all Collection Units from natural lakes. The top levelDepositional Environments, with some examples, are:

• Archaeological burials, middens, mounds

• Biological packrat middens, dung, moss polsters



• Estuarine mangrove swamps, salt marshes

• Lacustrine lakes and ponds

• Marine deep sea benthic, coastal bars

• Palustrine wetlands including fens, bogs, and marshes

• Riverine river channels, point bars, natural levees

• Sampler Tauber traps for modern pollen samples

• Spring tufa deposits, spring conduits

• Terrestrial caves, rock shelters, colluvium, volcanic deposits, soils

The Depositional Environment may change through time. For example, as a basin fills with sediment, it may convertfrom a lake to a fen and perhaps later to a bog. A colluvial slope may have alluvial sediments at depth. A modernplaya lake may have a buried paleosol. Thus, a sediment section may have units with different facies and depositionalagents. The Facies is the sum total of the characteristics that distinguish a sedimentary unit. Facies are listed in theFaciesTypes lookup table and are assigned to Analysis Units in the AnalysisUnits.FaciesID field. A sedimentary unitmay have one or more agents of deposition. For example, a cave deposit may be partly owing to human habitationand partly to carnivore activity. Depositional Agents are listed in the DepAgentTypes lookup table and are assigned toAnalysis Units in the DepAgents table.

Whereas Facies and Depositional Agents are both keyed to Analysis Units, the Lithology table is keyed to CollectionUnits. Analysis Units, especially from cores, may not be contiguous but be placed at discrete intervals down section.Lithologic units are defined by depth in the Collection Unit. Whereas Facies have short descriptions and are keyedto the FaciesTypes lookup table, the Lithology.Description field is a memo, and lithologic descriptions much moredetailed than Facies descriptions. FAUNMAP, which was built around Analysis Units, stores Facies and DepositionalAgent data; whereas the pollen database, which was centered on Collection Units, stores lithologic data.

1.2.7 Date Fields

Neotoma uses date fields in several tables. Dates are stored internally as a double precision floating point number,which facilitates calculations and functions involving dates. The disadvantage is that complete dates must be stored,i.e. year, month, and day; whereas in many cases only the year or month are known, for example the month a corewas collected. Neotoma had adapted the convention that if only the month is known, the day is set to the first ofthe month; if only the year is known, the month and day are set to January 1. Thus, «June 1984» is set to «June 1,1984»; and «1984» is set to «January 1, 1984». The drawback, of course, is that these imprecise dates cannot bedistinguished from precise dates on the first of the month. However, it was determined that the advantages of the datefields outweighed this disadvantage.

1.3 SQL Quickly

SQL (Sturctured Query Language) is a standard language for querying and modifying relational databases. It is anANSI and ISO standard, although various vendors have added proprietary extensions. It is beyond the scope of thisdocument to describe SQL or the differences between Microsoft Access SQL and ANSI SQL. However, examplesof SQL queries are provided in this document as a tutorial. Most users of Access probably use the graphical designview for queries, but SQL queries are better suited for examples. These queries can by typed or copied and pastedinto the Access query SQL view. The query can then be executed or opened in design view to show the graphicalrepresentation. One difference between Access SQL and other flavors is the wildcard; Access uses * rather than %.

1.3. SQL Quickly 15


1.3.1 SQL Example

The following SQL example lists the number of sites by GeoPoliticalID (the name of the country) for and GeoPoliti-calID that is defined as a country.

1 SELECT2 COUNT(sites.SiteName),3 gpu.GeoPoliticalName,4 gpu.GeoPoliticalUnit5 FROM6 (7 SELECT8 *9 FROM

10 GeoPoliticalUnits11 WHERE12 geopoliticalunits.GeoPoliticalUnit = "country"13 ) AS gpu14 INNER JOIN (15 Sites16 INNER JOIN SiteGeoPolitical ON Sites.SiteID = SiteGeoPolitical.SiteID17 ) ON gpu.GeoPoliticalID = SiteGeoPolitical.GeoPoliticalID18 GROUP BY19 gpu.GeoPoliticalID,20 gpu.GeoPoliticalUnit;

1.3.2 Table Keys

Within tables there are often Keys. A Key may be a Primary Key (PK), which acts as a unique identifier for individualrecords within a table, or they may be a Foreign Key (FK) which refers to a unique identifier in another table. PrimaryKeys and Foreign Keys are critical to join tables in a SQL query. In the above example we can see that the

1.3.3 Data Types

In the table descriptions in the following section, the SQL Server data types are given for field descriptions. Theequivalent Access data types are given in the following table.

SQL Server data type Access data typebit Yes/Nodatetime Date/Timefloat Doubleint Long Integernvarchar(n), where n = 1 to 4000 Textnvarchar(MAX) Memo

1.4 Neotoma Tables

The Neotoma Database contains more than 100 tables and as new proxy types get added, or new metadata is stored,the number of tables may increase or decrease. As a result, this manual should not be considered the final authority,but it should provide nearly complete coverage of the database and its structure. In particular, do our best to dividetables into logical groupings: Chronology & Age related tables, Dataset related tables, Site related tables, Contacttables, Sample tables and so on.



1.4.1 Site Related Tables

Tables for key geographic information relating to the dataset. Specifically geographic coordinates, geo-political unitsand any situational information such as images of the site itself.

SiteImages, Sites, SiteGeoPolitical, GeoPoliticalUnits

1.4.2 Dataset & Collection Related Tables

Tables related to complete datasets, or collections of samples. These include Collection information, but only refer tosites, since, as described in the Design Concepts, datasets are conceptually nested within sites, even if a site containsonly a single dataset.

AggregateDatasets, AggregateOrderTypes, CollectionTypes, CollectionUnits, DatasetPublications,Datasets, DatasetSubmissions, DatasetSubmissionTypes, DatasetTypes, DatasetPIs, DepEnvtTypes,Lithology, Projects.

1.4.3 Chronology & Age Related Tables

Information about the age models and chronological controls used to assess sample ages. Includes secondary infor-mation on tephras, and geochronological data types.

AgeTypes, AggregateChronologies, ChronControls, ChronControlTypes, Chronologies, Aggregate-SampleAges, Geochronology, GeochronPublications, GeochronTypes, RelativeAgePublications,RelativeAges, RadiocarbonCalibration, RelativeAgeScales, RelativeAgeUnits, RelativeChronology,Tephrachronology, Tephras.

1.4.4 Sample Related Tables

Information relating to individual samples or analysis units. This includes the age of the sample, the data content ofthe sample, and information relating to the physical condition or situation of the samples themselves.

AnalysisUnits, Data, DepAgents, DepAgentTypes, SampleAges, SampleAnalysts, SampleKeywords, Sam-ples, AggregateSamples, FaciesTypes, Keywords.

1.4.5 Taxonomy Related Tables

Tables related to taxonomic information, phylogenetic information and ecological classifications. These tables alsoinclude hierarchy based on morphological or phylogenetic relationships.

EcolGroups, EcolGroupTypes, EcolSetTypes, Synonyms, SynonymTypes, Taxa, TaxaGroupTypes, Vari-ables, VariableContexts, VariableElements, VariableModifications, VariableUnits.

1.4.6 Individual Related Tables

Tables associated with individuals, institutions and organizations.

Collectors, Contacts, ContactStatuses

1.4. Neotoma Tables 17


1.4.7 Publication Related Tables

Information relating to the publication of primary or derived data within the Neotoma Paleoecological Database.

PublicationAuthors, PublicationEditors, Publications, PublicationTypes

1.5 Chronology & Age Related Tables

1.5.1 AgeTypes

Lookup table of Age Types or units. This table is referenced by the Chronologies and Geochronology tables.

Field Name Variable Type KeyAgeTypeID int PKAgeType nvarchar(64)

AgeTypeID (Primary Key) An arbitrary Age Type identification number.

AgeType Age type or units. Includes the following:

• Calendar years AD/BC

• Calendar years BP

• Calibrated radiocarbon years BP

• Radiocarbon years BP

• Varve years BP

1.5.2 AggregateChronologies

This table stores metadata for Aggregate Chronologies. An Aggregate Chronology refers to an explicit chronologyassigned to a sample Aggregate. The individual Aggregate Samples have ages assigned in the AggregateSampleAgestable. An Aggregate Chronology would be used, for example, for a set of packrat middens assigned to an Aggregate-Datasets. The Aggregate Chronology is analsgous to the Chronology assigned to samples from a single CollectionUnit.

An Aggregate may have more than one Aggregate Chronology, for example one in radiocarbon years and another incalibrated radiocarbon years. One Aggreagate Chronology per Age Type may be designated the default, which is theAggregate Chronology currently preferred by the database stewards.

Field Name Variable Type Key Reference TableAggregateChronID int PKAggregateDatasetID int FK AggregateDatasetsAgeTypeID int FK AgeTypesIsDefault bitChronologyName nvarchar(80)AgeBoundYounger intAgeBoundOlder intNotes nvarchar(MAX)

AggregateChronID (Primary Key) An arbitrary Aggregate Chronology identification number.

AggregateDatasetID (Foreign Key) Dataset to which the Aggregate Chronology applies. Field links to the Aggre-gateDatasets table.

AgeTypeID (Foreign Key) Age type or units. Field links to the AgeTypes table.



IsDefault Indicates whether the Aggregate Chronology is a default or not. Default status is determined by a Neotomadata steward. Aggregate Datasets may have more than one default Aggregate Chronology, but may have onlyone default Aggregate Chronology per Age Type.

ChronologyName Optional name for the Chronology.

AgeBoundYounger The younger reliable age bound for the Aggregate Chronology. Younger ages may be assignedto samples, but are not regarded as reliable. If the entire Chronology is considered reliable, AgeBoundYoungeris assigned the youngest sample age rounded down to the nearest 10. Thus, for 72 BP, AgeBoundYounger = 70BP; for -45 BP, AgeBoundYounger = -50 BP.

AgeBoundOlder The older reliable age bound for the Aggregate Chronology. Ages older than AgeOlderBound maybe assigned to samples, but are not regarded as reliable. This situation is particularly true for ages extrapolatedbeyond the oldest Chron Control. . If the entire Chronology is considered reliable, AgeBoundOlder is assignedthe oldest sample age rounded up to the nearest 10. Thus, for 12564 BP, AgeBoundOlder is 12570.

Notes Free form notes or comments about the Aggregate Chronology.

1.5.3 ChronControls

This table stores data for Chronology Controls, which are the age-depth control points used for age models. Thesecontrols may be geophysical controls, such as radiocarbon dates, but include many other kinds of age controls, such asbiostratigraphic controls, archaeological cultural associations, and volcanic tephras. In the case of radiocarbon dates, aChronology Control may not simply be the raw radiocarbon date reported by the laboratory, but perhaps a radiocarbondate corrected for an old carbon reservoir, a calibrated radiocarbon date, or an average of several radiocarbon datesfrom the same level. A common control for lake-sediment cores is the age of the top of the core, which may be theyear the core was taken or perhaps an estimate of 0 BP if a few cm of surficial sediment were lost.

Field Name Variable Type Key Reference TableChronControlID int PKChronologyID int FK ChronologiesChronControlTypeID int FK ChronControlTypesDepth floatThickness floatAge floatAgeLimitYounger floatAgeLimitOlder floatNotes ntext

ChronControlID (Primary Key) An arbitrary Chronology Control identification number.

ChronologyID (Foreign Key) Chronology to which the ChronControl belongs. Field links to the Chronolgies table.

ChronControlTypeID (Foreign Key) The type of Chronology Control. Field links to the ChronControlTypes table.

Depth Depth of the Chronology Control in cm.

Thickness Thickness of the Chronology Control in cm.

Age Age of the Chronology Control.

AgeLimitYounger The younger age limit of a Chronology Control. This limit may be explicitly defined, for examplethe younger of the 2-sigma range limits of a calibrated radiocarbon date, or it may be more loosely defined, forexample the younger limit on the range of dates for a biostratigraphic horizon.

AgeLimitOlder The older age limit of a Chronology Control.

Notes Free form notes or comments about the Chronology Control.

1.5. Chronology & Age Related Tables 19


1.5.4 ChronControlTypes

Lookup table of Chronology Control Types. This table is referenced by the ChronControls table.

Field Name Variable Type KeyChronControlTypeID int PKChronControlType nvarchar(50)

ChronControlTypeID (Primary Key) An arbitrary Chronology Control Type identification number.

ChronControlType The type of Chronology Control object. Chronology Controls include such geophysical controlsas radiocarbon dates, calibrated radiocarbon dates, averages of several radiocarbon dates, potassium-argon dates,and thermoluminescence dates, as well as biostratigraphic controls, sediment stratigraphic contols, volcanictephras, archaeological cultural associations, and any other types of age controls. In general these are calibratedor calendar year dates Before Present. Some ChronControlTypes are in Radiocarbon Years, so caution must beexercised.

1.5.5 Chronologies

This table stores Chronology data. A Chronology refers to an explicit chronology assigned to a Collection Unit. AChronology has Chronology Controls, the actual age-depth control points, which are stored in the ChronControlstable. A Chronology is also based on an Age Model, which may be a numerical method that fits a curve to a set ofage-depth control points or may simply be individually dated Analysis Units.

A Collection Unit may have more than one Chronology, for example one in radiocarbon years and another in cali-brated radiocarbon years. There may be a Chronology developed by the original author and another developed by alater research project. Chronologies may be stored for archival reasons, even though they are now believed to haveproblems, if they were used for an important research project. One Chronology per Age Type may be designated thedefault Chronology, which is the Chronology currently preferred by the database stewards.

Based upon the Chronology, which includes the Age Model and the Chron Controls, ages are assigned to individualsamples, which are stored in the SampleAges table.

A younger and older age bounds are assigned to the Chronology. Within these bounds the Chronology is regarded asreliable. Ages may be assigned to samples beyond the reliable age bounds, but these are not considered reliable.

Field Name Variable Type Key Reference TableChronologyID int PKCollectionUnitID int FK CollectionUnitsAgeTypeID int FK AgeTypesContactID int FK ContactsIsDefault bitChronologyName nvarchar(80)DatePrepared datetimeAgeModel nvarchar(80)AgeBoundYounger intAgeBoundOlder intNotes ntext

ChronologyID (Primary Key) An arbitrary Chronology identification number.

CollectionUnitID (Foreign Key) Collection Unit to which the Chronology applies. Field links to the CollectionUnitstable.

AgeTypeID (Foreign Key) Age type or units. Field links to the AgeTypes table.

ContactID (Foreign Key) Person who developed the Age Model. Field links to the Contacts table.



IsDefault Indicates whether the Chronology is a default chronology or not. Default status is determined by a Neotomadata steward. Collection Units may have more than one default Chronology, but may have only one defaultChronology per Age Type. Thus, there may be a default radiocarbon year Chronology and a default calibratedradiocarbon year Chronology, but only one of each. Default Chronologies may be used by the Neotoma website, or other web sites, for displaying default diagrams or time series of data. Default Chronologies may alsobe of considerable use for actual research purposes; however, users may of course choose to develop their ownchronologies.

ChronologyName Optional name for the Chronology. Some examples are:

• COHMAP chron 1 A Chronology assigned by the COHMAP project.

• COHMAP chron 2 An alternative Chronology assigned by the COHMAP project

• NAPD 1 A Chronology assigned by the North American Pollen Database.

• Gajewski 1995 A Chronology assigned by Gajewski (1995).

DatePrepared Date that the Chronology was prepared.

AgeModel The age model used for the Chronology. Some examples are: linear interpolation, 3rd order polynomial,and individually dated analysis units.

AgeBoundYounger The younger reliable age bound for the Chronology. Younger ages may be assigned to samples,but are not regarded as reliable. If the entire Chronology is considered reliable, AgeBoundYounger is assignedthe youngest sample age rounded down to the nearest 10. Thus, for 72 BP, AgeBoundYounger = 70 BP; for -45BP, AgeBoundYounger = -50 BP.

AgeBoundOlder The older reliable age bound for the Chronology. Ages older than AgeOlderBound may be assignedto samples, but are not regarded as reliable. This situation is particularly true for ages extrapolated beyond theoldest Chron Control. . If the entire Chronology is considered reliable, AgeBoundOlder is assigned the oldestsample age rounded up to the nearest 10. Thus, for 12564 BP, AgeBoundOlder is 12570.

Notes Free form notes or comments about the Chronology.

SQL Example

The following SQL statement produces a list of Chronologies for :

1 SELECT Sites.SiteName, Chronologies.ChronologyName,2 Chronologies.IsDefault, AgeTypes.AgeType3

4 FROM AgeTypes INNER JOIN ((Sites INNER JOIN CollectionUnits ON5 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN Chronologies ON6 CollectionUnits.CollectionUnitID = Chronologies.CollectionUnitID) ON7 AgeTypes.AgeTypeId = Chronologies.AgeTypeID8

9 WHERE (((Sites.SiteName)=""));

Result:

SiteName ChronologyName IsDefault AgeTypeCOHMAP chron 1 FALSE Radiocarbon years BPNAPD 1 TRUE Radiocarbon years BPNAPD 2 TRUE Calibrated radiocarbon years BP



SQL Example

The following statement produces a list of the ChronControls for the Default Chronology from in Calibrated radiocar-bon years BP. Here we show only the first 5 rows:

1 SELECT ChronControls.Depth, ChronControls.Age,2 ChronControls.AgeLimitYounger, ChronControls.AgeLimitOlder,3 ChronControlTypes.ChronControlType4

5 FROM ChronControlTypes INNER JOIN ((AgeTypes INNER JOIN ((Sites INNER6 JOIN CollectionUnits ON Sites.SiteID = CollectionUnits.SiteID) INNER7 JOIN Chronologies ON CollectionUnits.CollectionUnitID =8 Chronologies.CollectionUnitID) ON AgeTypes.AgeTypeId =9 Chronologies.AgeTypeID) INNER JOIN ChronControls ON

10 Chronologies.ChronologyID = ChronControls.ChronologyID) ON11 ChronControlTypes.ChronControlTypeID = ChronControls.ChronControlTypeID12

13 WHERE (((Sites.SiteName)="Wolsfeld Lake") AND14 ((Chronologies.IsDefault)=True) AND ((AgeTypes.AgeType)="Calibrated15 radiocarbon years BP"));

Result:

Depth Age AgeLimitYounger AgeLimitOlder ChronControlType650 -25 -25 -25 Core top662 -13 -8 -18 Interpolated, corrected for compaction670 0 -5 5 Interpolated, corrected for compaction680 22 17 27 Interpolated, corrected for compaction690 46 41 51 Interpolated, corrected for compaction. . . . . . . . . . . . . . .

1.5.6 AggregateSampleAges

This table stores the links to the ages of samples in an Aggregate Dataset. The table is necessary because samples maybe from Collection Units with multiple chronologies, and this table stores the links to the sample ages desired for theAggregate Dataset.

Field Name Variable Type Key Reference TableAggregateDatasetID int PK, FK AggregateDatasetsAggregateChronID int PK, FK AggregateChronologiesSampleAgeID int PK, FK SampleAges

AggregateDatasetID (Primary Key, Foreign Key) Aggregate Dataset identification number. Field links to the Ag-gregateDatasets table.

AggregateChronID (Primary Key, Foreign Key) Aggregate Chronology identification number Field links to theAggregateChronologies table.

SampleAgeID (Primary Key, Foreign Key) Sample Age ID number. Field links to the SampleAges table.

SQL Example

The following SQL statement produces a list of Sample ID numbers and ages for the Aggregate Dataset:

1 SELECT AggregateSamples.SampleID, SampleAges.Age2

3 FROM SampleAges INNER JOIN ((AggregateDatasets INNER JOIN



4 AggregateSampleAges ON AggregateDatasets.AggregateDatasetID =5 AggregateSampleAges.AggregateDatasetID) INNER JOIN AggregateSamples ON6 AggregateDatasets.AggregateDatasetID =7 AggregateSamples.AggregateDatasetID) ON (AggregateSamples.SampleID =8 SampleAges.SampleID) AND (SampleAges.SampleAgeID =9 AggregateSampleAges.SampleAgeID)

10

11 WHERE (((AggregateDatasets.AggregateDatasetName)=""));

SQL Example

The AggregateSampleAges table may have multiple SampleAgeID’s for Aggregate Dataset samples, for exampleSampleAgeID’s for radiocarbon and calibrated radiocarbon chronologies. In this case, the Chronolgies table mustbe linked into a query to obtain the ages of Aggregate Samples, and either the AgeTypeID must be specified in theChronolgies table or the AgeTypes table must also be linked with the AgeType specified. The following SQL statementproduces a list of Sample ID numbers and «Radiocarbon years BP» ages for the «» Aggregate Dataset: Samples

1 SELECT AggregateSamples.SampleID, SampleAges.Age2

3 FROM AgeTypes INNER JOIN (Chronologies INNER JOIN (SampleAges INNER JOIN4 ((AggregateDatasets INNER JOIN AggregateSampleAges ON5 AggregateDatasets.AggregateDatasetID =6 AggregateSampleAges.AggregateDatasetID) INNER JOIN AggregateSamples ON7 AggregateDatasets.AggregateDatasetID =8 AggregateSamples.AggregateDatasetID) ON (AggregateSamples.SampleID =9 SampleAges.SampleID) AND (SampleAges.SampleAgeID =

10 AggregateSampleAges.SampleAgeID)) ON Chronologies.ChronologyID =11 SampleAges.ChronologyID) ON AgeTypes.AgeTypeId = Chronologies.AgeTypeID12

13 WHERE (((AggregateDatasets.AggregateDatasetName)="") AND14 ((AgeTypes.AgeType)="Radiocarbon years BP"));

1.5.7 Geochronology

This table stores geochronologic data. Geochronologic measurements are from geochronologic samples, which arefrom Analysis Units, which may have a depth and thickness. Geochronologic measurments may be from the same ordifferent Analysis Units as fossils. In the case of faunal excavations, geochronologic samples are typically from thesame Analysis Units as the fossils, and there may be multiple geochronologic samples from a single Analysis Unit. Inthe case of cores used for microfossil analyses, geochronologic samples are often from separate Analysis Units; datedcore sections are often thicker than microfossil Analysis Units.

| GeochronID | Long Integer | PK | | +—————————-+—————-+——+————————+ | Sam-pleID | Long Integer | FK | Samples | +—————————-+—————-+——+————————+ | Geochron-TypeID | Long Integer | FK | GeochronTypes | +—————————-+—————-+——+————————+ |AgeTypeID | Long Integer | FK | AgeTypes | +—————————-+—————-+——+————————+ |Age | Double | | | +—————————-+—————-+——+————————+ | ErrorOlder | Double | | | +—————————-+—————-+——+————————+ | ErrorYounger | Double | | | +—————————-+—————-+——+————————+ | Infinite | Yes/No | | | +—————————-+—————-+——+————————+ | Delta13C | Double | | | +—————————-+—————-+——+————————+| LabNumber | Text | | | +—————————-+—————-+——+————————+ | MaterialDated | Text || | +—————————-+—————-+——+————————+ | Notes | Memo | | | +—————————-+—————-+——+————————+

GeochronID (Primary Key) An arbitrary Geochronologic identificantion number.



SampleID (Foreign Key) Sample identification number. Field links to Samples table.

GeochronTypeID (Foreign Key) Identification number for the type of Geochronologic analysis, e.g. «Carbon-14»,«Thermoluminescence». Field links to the GeochronTypes table.

AgeTypeID (Foreign Key) Identification number for the age units, e.g. «Radiocarbon years BP», «Calibrated radio-carbon years BP».

Age Reported age value of the geochronologic measurement.

ErrorOlder The older error limit of the age value. For a date reported with ±1 SD or 𝜎, the ErrorOlder and ErrorY-ounger values are this value.

ErrorYounger The younger error limit of the age value.

Infinite Is «True» for and infinite or “greater than” geochronologic measurement, otherwise is «False».

Delta13C The measured or assumed 𝛿13C value for radiocarbon dates, if provided. Radiocarbon dates are assumedto be normalized to 𝛿13C, and if uncorrected and normalized ages are reported, the normalized age should beentered in the database.

LabNumber Lab number for the geochronologic measurement.

Material Dated Material analyzed for a geochronologic measurement.

Notes Free form notes or comments about the geochronologic measurement.

SQL Example

This query lists the geochronologic data for Montezuma Well.

1 SELECT AnalysisUnits.Depth, AnalysisUnits.Thickness,2 GeochronTypes.GeochronType, Geochronology.Age, Geochronology.ErrorOlder,3 Geochronology.ErrorYounger, Geochronology.Delta13C,4 Geochronology.LabNumber, Geochronology.MaterialDated,5 Geochronology.Notes6

7 FROM GeochronTypes INNER JOIN ((((Sites INNER JOIN CollectionUnits ON8 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN AnalysisUnits ON9 CollectionUnits.CollectionUnitID = AnalysisUnits.CollectionUnitID) INNER

10 JOIN Samples ON AnalysisUnits.AnalysisUnitID = Samples.AnalysisUnitID)11 INNER JOIN Geochronology ON Samples.SampleID = Geochronology.SampleID)12 ON GeochronTypes.GeochronTypeID = Geochronology.GeochronTypeID13

14 WHERE (((Sites.SiteName)="Montezuma Well"));

Result:



DepthThick..GeochronType Age ErrorOlder

ErrorYounger

Delta13CLabNum-ber

Mate-rial-Dated

Notes

1015 1 Carbon-14:accelerator massspectrometry

1097595 95 AA-4694

Junipe-rustwig


1526 50 50 AA-2450

char-coal,wood


2885 60 60 AA-2451

char-coal,wood


5540 60 60 AA-4693

char-coal,wood


8003 70 70 AA-2452

Scirpusachenes

535 10 Carbon-14:proportional gascounting

14950350 320 -26.7

A-4732

bark Davis and Shafer(1992) reject as tooold.

887 1 Carbon-14:proportional gascounting

9520 200 200 -25.3

A-4733

wood


24910370 370 AA-5053

wood Davis and Shafer(1992) reject as tooold.

1.5.8 GeochronPublications

Publications in which Geochronologic measurements are reported. Many older radiocarbon dates are reported in thejournal Radiocarbon. Dates may be reported in multiple publications. The “publication” could be a database such asthe online Canadian Archaeological Radiocarbon Database.

Field Name Variable Type Key Reference TableGeochronID Long Integer PK, FK GeochronologyPublicationID Long Integer PK, FK Publications

GeochronID (Primary Key, Foreign Key) Geochronologic identification number. Field links to the Geochronologytable.

PublicationID (Primary Key, Foreign Key) Publication identification number. Field links to the Publications table.

1.5.9 GeochronTypes

Lookup table for Geochronology Types. Table is referenced by the Geochronology table.

Field Name Variable Type KeyGeochronTypeID Long Integer PKGeochronType Text

GeochronTypeID (Primary Key) Geochronology Type identification number.

GeochronType Type of Geochronologic measurement.



1.5.10 RelativeAgePublications

This table stores Publications in which Relative Ages are reported for CollectionUnits.

Field Name Variable Type Key Reference TableRelativeAgeID Long Integer PK, FK RelativeAgesPublicationID Long Integer PK, FK Publications

RelativeAgeID (Primary Key, Foreign Key) Relative Ages identification number. Field links to the RelativeAgestable.

PublicationID (Primary Key, Foreign Key) Publication identification number. Field links to Publications table.

1.5.11 RelativeAges

Lookup table of RelativeAges. Table is referenced by the RelativeChronology table.

Field Name Variable Type Key Reference TableRelativeAgeID Long Integer PKRelativeAgeUnitID Long Integer FK RelativeAgeUnitsRelativeAgeScaleID Long Integer FK RelativeAgeScalesRelativeAge TextC14AgeYounger DoubleC14AgeOlder DoubleCalAgeYounger DoubleCalAgeOlder DoubleNotes Memo

RelativeAgeID (Primary Key) An arbitrary Relative Age identification number.

RelativeAgeUnitID (Foreign Key) Relative Age Unit (e.g. «Marine isotope stage», «Land mammal age»). Fieldlinks to the RelativeAgeUnits lookup table.

RelativeAgeScaleID (Foreign Key) Relative Age Scale (e.g. «Geologic time scale», «Marine isotope stages»). Fieldlinks to the RelativeAgeScales lookup table.

RelativeAge Relative Age (e.g. «Rancholabrean», a land mammal age; «MIS 11», marine isotope stage 11).

C14AgeYounger Younger age of the Relative Age unit in 14C yr B.P. Applies only to Relative Age units within theradiocarbon time scale.

C14AgeOlder Older age of the Relative Age unit in 14C yr B.P. Applies only to Relative Age units within the radio-carbon time scale.

CalAgeYounger Younger age of the Relative Age unit in calendar years.

CalAgeOlder Older age of the Relative age unit in calendar years.

Notes Free form notes or comments about Relative Age unit.

SQL Example

The following query gives the Relative Ages for the «North American land mammal ages». The Relative Age Unitfor each of these is «Land mammal age». Commas were added to the ages in the query result to make them morereadable.



1 SELECT RelativeAges.RelativeAge, RelativeAges.CalAgeYounger,2 RelativeAges.CalAgeOlder3

4 FROM RelativeAgeScales INNER JOIN RelativeAges ON5 RelativeAgeScales.RelativeAgeScaleID = RelativeAges.RelativeAgeScaleID6

7 WHERE (((RelativeAgeScales.RelativeAgeScale)="North American land mammal ages"));

Result:

CalAgeYounger CalAgeOlder RelativeAge14000.0 150000.0 Rancholabrean150000.0 1900000.0 Irvingtonian850000.0 1900000.0 Irvingtonian I400000.0 850000.0 Irvingtonian II150000.0 400000.0 Irvingtonian III1900000.0 4900000.0 Blancan4620000.0 4900000.0 Blancan I4100000.0 4620000.0 Blancan II3000000.0 4100000.0 Blancan III2500000.0 3000000.0 Blancan IV1900000.0 2500000.0 Blancan V4900000.0 9000000.0 Hemphillian10000.0 14000.0 Early Santarosean410.0 10000.0 Late Santarosean-100.0 410.0 Saintaugustinean410.0 14000.0 Santarosean

1.5.12 RadiocarbonCalibration

Radiocarbon calibraton table. This table is intended for quick calibraton of age-model radiocarbon dates. Thesecalibrated dates are for perusal and data exploration only. Please see Section 2.5 for a full discussion.

Field Name Variable Type KeyC14yrBP Long Integer PKCalyrBP Long Integer

C14yrBP Age in radiocarbon years BP. The range is -100 to 45,000 by 1-year increments.

CalyrBP Age in calibrated radiocarbon years BP.

1.5.13 RelativeAgeScales

Lookup table of Relative Age Scales. Table is referenced by the RelativeAges table.

Field Name Variable Type KeyRelativeAgeScaleID Long Integer PKRelativeAgeScale Text

RelativeAgeScaleID (Primary Key) An arbitrary Relative Age Scale identification number.

RelativeAgeScale Relative Age Scale. The table stores the following Relative Age Scales:

• Archaeological time scale

• Geologic time scale



• Geomagnetic polarity time scale

• Marine isotope stages

• North American land mammal ages

• Quaternary event classification

1.5.14 RelativeAgeUnits

Lookup table of RelativeAgeUnits. Table is referenced by the RelativeAges table.

Field Name Variable Type KeyRelativeAgeUnitID Long Integer PKRelativeAgeUnit Text

RelativeAgeUnitID (Primary Key) An arbitrary Relative Age Unit identification number.

RelativeAgeUnit Relative Age Unit. Below are the Relative Age Units for the «Geologic time scale» with an exampleRelative Age.

Geologic time scaleRelativeAgeUnit RelativeAge ExamplePeriod QuaternaryEpoch PleistoceneStage Middle PleistoceneInformal stage Middle Holocene

«Period», «Epoch», and «Stage» are defined by the International Commission on Statigraphy. An «Informal stage» isdefined in Neotoma.

1.5.15 RelativeChronology

This table stores relative chronologic data. Relative Ages are assigned to Analysis Units, The Relative Age data alongwith any possible Geochronology and Tephrachronology data are used to create a chronology.

Field Name Variable Type Key Reference TableRelativeChronID Long Integer PKAnalysisUnitID Long Integer FK AnalysisUnitsRelativeAgeID Long Integer FK RelativeAgesNotes Memo

RelativeChronID (Primary Key) An arbitrary Relative Chronology identification number.

AnalysisUnitID (Foreign Key) Analysis Unit identification number. Field links to the AnalysisUnits table.

RelativeAgeID (Foreign Key) Relative Age identification number. Field links to the RelativeAges lookup table.

Notes Free form notes or comments.

1.5.16 Tephrachronology

This table stores tephrachronologic data. The table relates Analysis Units with dated tephras in the Tephras table.These are tephras with established ages that are used form a chronology. The tephras are typically not directly datedat the Site of the Analysis Unit, but have been dated at other sites. A directly dated tephra, e.g. an argon-argon date,belongs in the Geochronology table.



Field Name Variable Type Key Reference TableTephrachronID Long Integer PKAnalysisUnitID Long Integer FK AnalysisUnitsTephraID Long Integer FK TephrasNotes Memo

TephrachronID (Primary Key) An arbitrary Tephrachronology identification number.

AnalysisUnitID (Foreign Key) Analysis Unit identification number. Field links to the AnalysisUnits table. The tephramay be contained within the AnalysisUnit, especially in excavations, or the AnalysisUnit may be assigned specificallyto the tephra, particulary with cores.

TephraID (Foreign Key) Tephra identification number. Field links to the Tephras table.

Notes Free form notes or comments about the tephra.

1.5.17 Tephras

Tephras lookup table. This table stores recognized tephras with established ages. Referenced by the Tephrachronologytable.

Field Name Variable Type KeyTephraID Long Integer PKTephraName TextC14Age DoubleC14AgeYounger DoubleC14AgeOlder DoubleCalAge DoubleCalAgeYounger DoubleCalAgeOlder DoubleNotes Memo

TephraID (Primary Key) An arbitrary Tephra identification number.

TephraName Name of the tephra, e.g. «Mazama».

C14Age Age of the tephra in 14C yr BP. For example, Hallett et al. (1997) provide an estimate of the age of theMazama tephra based on radiocarbon dating of plant macrofossils in lake sediments encasing the tephra.

C14AgeYounger Younger age estimate of the tephra in 14C yr BP.

C14AgeOlder Older age estimate of the tephra in 14C yr BP.

CalAge Age of the tephra in cal yr BP, either calibrated radiocarbon years or estimated calendar years derived fromanother dating method. For example, Zdanowicz et al. (1999) identified the Mazama tephra in the GISP2 icecore and estimated the age from layer counts.

CalAgeYounger Younger age estimate of the tephra in cal yr BP.

CalAgeOlder Older age estimate of the tephra in cal yr BP.

Notes Free form notes or comments about the tephra.

1.6 Dataset & Collection Related Tables

1.6.1 AggregateDatasets

Aggregate Datasets are aggregates of samples of a particular datatype. Some examples:

1.6. Dataset & Collection Related Tables 29


• Plant macrofossil samples from a group of packrat middens collected from a particular valley, mountain range,or other similarly defined geographic area. Each midden is from a different Site or Collection Unit, but they aregrouped into time series for that area and are published as single dataset.

• Samples collected from 32 cutbanks along several kms of road. Each sample is from a different site, but theyform a time series from 0 – 12,510 14C yr BP, and pollen, plant macrofossils, and beetles were published andgraphed as if from a single site.

• A set of pollen surface samples from particular region or study that were published and analyzed as a singledataset and submitted to the database as a single dataset.

• The examples above are datasets predefined in the database. New aggregate datasets could be assembled forparticular studies, for example all the pollen samples for a given time slice for a given geographic region.

AggregateDatasetsAggregateDatasetID int PKAggregateDatasetName nvarchar(255)AggregateOrderTypeID int FK AggregateOrderTypesNotes nvarchar(MAX)

AggregateDatasetID (Primary Key) An arbitrary Aggregate Dataset identification number.

AggregateDatasetName Name of Aggregate Dataset.

AggregateOrderTypeID (Foreign Key) Aggregate Order Type identification number. Field links to the Aggregate-OrderTypes lookup table.

Notes Free form notes about the Aggregate Order Type.

1.6.2 AggregateOrderTypes

Lookup table for Aggregate Order Types. Table is referenced by the AggregateDatasets table.

AggregateOrderTypesAggregateOrderTypeID int PKAggregateOrderType nvarchar(60)Notes ntext

AggregateOrderTypeID (Primary Key) An arbitrary Aggregate Order Type identification number.

AggregateOrderType The Aggregate Order Type.

Notes Free form notes or comments about the Aggregate Order Type.

The Aggregate Order Types are:

• Latitude: AggregateDataset samples are ordered by, in order of priority, either (1) CollectionUnits.GPSLatitudeor (2) the mean of Sites.LatitudeNorth and Sites.LatitudeSouth.

• Longitude AggregateDataset samples are ordered by, in order of priority, either (1) Collectio-nUnits.GPSLongitude or (2) the mean of Sites.LongitudeWest and Sites.LongitudeEast.

• Altitude AggregateDataset samples are ordered by Sites.Altitude‘.

• Age AggregateDataset samples are ordered by SampleAges.Age, where SampleAges.SampleAgeID is from Ag-gregateSampleAges.SampleAgeID.

• Alphabetical by site name AggregateDataset samples are ordered alphabetically by Sites.SiteName.

• Alphabetical by collection unit name AggregateDataset samples are ordered alphabetically by Collectio-nUnits.CollUnitName.



• Alphabetical by collection units handle AggregateDataset samples are ordered alphabetically by Collectio-nUnits.Handle.

1.6.3 CollectionTypes

This table is a lookup table of for types of Collection Units, or Collection Types. Table is referenced by the Collectio-nUnits table.

CollectionTypes Variable Type KeyCollTypeID int PKCollType nvarchar(50)

CollTypeID (Primary Key) An arbitrary Collection Type identification number.

Colltype The Collection Type. Types include cores, sections, excavations, and animal middens. Collection Units maybe modern collections, surface float, or isolated specimens. Composite Collections Units include different kindsof Analysis Units, for example a modern surface sample for ostracodes and an associated water sample.

1.6.4 CollectionUnits

This table stores data for Collection Units.

CollectionUnits Variable Type Key Table ReferenceCollectionUnitID int PKHandle nvarchar(10)SiteID int FK SitesCollTypeID int FK CollectionTypesDepEnvtID int FK DepEnvtTypesCollUnitName nvarchar(255)CollDate datetimeCollDevice nvarchar(255)GPSLatitude floatGPSLongitude floatGPSAltitude floatGPSError floatWaterDepth floatSubstrateID int FK SubstratesSlopeAspect intSlopeAngle intLocation nvarchar(255)Notes ntext

CollectionUnitID (Primary Key) An arbitrary Collection Unit identification number.

SiteID (Foreign Key) Site where CollectionUnit was located. Field links to Sites table.

CollTypeID (Foreign Key) Type of Collection Unit. Field links to the CollectionTypes table.

DepEnvtID (Foreign Key) Depositional environment of the CollectionUnit. Normally, this key refers to the modernenvironment. For example, the site may be located on a colluvial slope, in which case the Depositional Environ-ment may be Colluvium or Colluvial Fan. However, an excavation may extend into alluvial sediments, whichrepresent a different depositional environment. These are accounted for by the Facies of the AnalysisUnit. Fieldlinks to the DepEnvtTypes table.

Handle Code name for the Collection Unit. This code may be up to 10 characters, but an effort is made to keep theseto 8 characters or less. Data are frequently distributed by Collection Unit, and the Handle is used for file names.



CollUnitName Name of the Collection Unit. Examples: Core BPT82A, Structure 9, P4A Test 57. If faunal data arereported from a site or locality without explicit Collection Units, then data are assigned to a single CollectionUnit with the name «Locality».

CollDate Date Collection Unit was collected.

CollDevice Device used for obtain Collection Unit. This field applies primarily to cores, for example «Wright square-rod piston corer (5 cm)».

GPSLatitude Precise latitude of the Collection Unit, typically taken with a GPS, although may be precisely measuredfrom a map.

GPSLongitude Precise longitude of the Collection Unit, typically taken with a GPS, although may be preciselymeasured from a map.

GPSAltitude Precise altitude of the Collection Unit, typically taken with a GPS or precisely obtained from a map.

GPSError Error in the horizontal GPS coordinates, if known.

WaterDepth Depth of water at the Collection Unit location. This field applies mainly to Collection Units from lakes.

SubstrateID (Foreign Key) Substrate or rock type on which the Collection Unit lies. Field links to the RockTypestable. This field is especially used for rodent middens.

SlopeAspect For Collection Units on slopes, the horizontal direction to which a slope faces measured in degreesclockwise from north. This field is especially used for rodent middens.

SlopeAngle For Collection Units on slopes, the angle of slope from horizontal. field is especially used for rodentmiddens.

Location Short description of the location of the Collection Unit within the site.

Notes Free form notes or comments about the Collection Unit.

1.6.5 DatasetPublications

This table lists the publications for datasets.

DatasetPublicationsDatasetID int PK, FK DatasetsPublicationID int PK, FK PublicationsPrimaryPub bit

DatasetID (Primary Key, Foreign Key) Dataset identification number. Field links to Datasets table.

PublicationID (Primary Key, Foreign Key) Publication identification number. Field links to Publications table.

PrimaryPub Is «True» if the publication is the primary publication for the dataset.

1.6.6 Datasets

This table stores the data for Datasets. A Dataset is the set of samples for a particular data type from a CollectionUnit. A Collection Unit may have multiple Datasets for different data types, for example one dataset for pollen andanother for plant macrofossils. Every Sample is assigned to a Dataset, and every Dataset is assigned to a CollectionUnit. Samples from different Collection Units cannot be assigned to the same Dataset (although they may be assignedto AggregateDatasets).



DatasetsDatasetID Long Integer PKCollectionUnitID Long Integer FK CollectionUnitsDatasetTypeID Long Integer FK DatasetTypesDatasetName TextNotes Memo

DatasetID (Primary Key) An arbitrary Dataset identification number.

CollectionUnitID (Foreign Key) Collection Unit identification number. Field links to the CollectionUnits table.

DatasetTypeID (Foreign Key) Dataset Type identification number. Field links to the DatasetTypes lookup table.

DatasetName Optional name for the Dataset.

Notes Free form notes or comments about the Dataset.

SQL Example

The following query lists the Dataset Types for the site «».

1 SELECT Sites.SiteName, DatasetTypes.DatasetType2

3 FROM DatasetTypes INNER JOIN ((Sites INNER JOIN CollectionUnits ON4 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN Datasets ON5 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) ON6 DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID7

8 WHERE (((Sites.SiteName)=""));

Result:

SiteName DatasetTypeLoss-on-ignitionpollengeochronologic

SQL Example

This query lists the plant macrofossils identified at site «Bear River No. 3».

1 SELECT Sites.SiteName, Taxa.TaxonName2

3 FROM DatasetTypes INNER JOIN (Taxa INNER JOIN (Variables INNER JOIN4 ((((Sites INNER JOIN CollectionUnits ON Sites.SiteID =5 CollectionUnits.SiteID) INNER JOIN Datasets ON6 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) INNER JOIN7 Samples ON Datasets.DatasetID = Samples.DatasetID) INNER JOIN Data ON8 Samples.SampleID = Data.SampleID) ON Variables.VariableID =9 Data.VariableID) ON Taxa.TaxonID = Variables.TaxonID) ON

10 DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID11

12 GROUP BY Sites.SiteName, DatasetTypes.DatasetType, Taxa.TaxonName13

14 HAVING (((Sites.SiteName)="Bear River No. 3") AND15 ((DatasetTypes.DatasetType)="plant macrofossils"));

Result:



SiteName TaxonNameBear River No. 3 Bolboschoenus maritimus subsp. paludosusBear River No. 3 Zea mays

1.6.7 DatasetSubmissions

Submissions to the database are of Datasets. Submissions may be original submissions, resubmissions, compilationsfrom other databases, or recompilations. See the description of the DatasetSubmissionTypes table.

DatasetSubmissionsSubmissionID Long Integer PKDatasetID Long Integer FK DatasetsProjectID Long Integer FK ProjectsContactID Long Integer FK ContactsSubmissionTypeID Long Integer FK DatasetSubmissionTypesSubmissionDate Date/TimeNotes Memo

SubmissionID (Primary Key) An arbitrary submission identification number.

DatasetID (Foreign Key) Dataset identification number. Field links to the Datasets table. Datasets may occur multi-ple times in this table (e.g. once for the original compilation into a different database and a second time for therecompilation into Neotoma).

ProjectID (Foreign Key) Database project responsible for the submission or compilation.

ContactID (Foreign Key) Contact identification number. Field links to the Contacts table. The Contact is the personwho submitted, resubmitted, compiled, or recompiled the data. This person is not necessarily the Dataset PI; itis the person who submitted the data or compiled the data from the literature.

SubmissionDate Date of the submission, resubmission, compilation, or recompilation.

SubmissionTypeID (Foreign Key) Submission Type identification number. Field links to the DatasetSubmission-Types table.

Notes Free form notes or comments about the submission.

1.6.8 DatasetSubmissionTypes

Lookup table of Dataset Submission Types. Table is referenced by the DatasetSubmissions table.

DatasetSubmissionTypesSubmissionTypeID Long Integer PKSubmissionType Text

SubmissionTypeID (Primary Key) An arbitrary Submission Type identification number.

SubmissionType Type of submission. The database has the following types:

• Original submission from data contributor

• Resubmission or revision from data contributor

• Compilation into a flat file database

• Compilation into a another relational database

• Recompilation or revisions to a another relational database

• Compilation into Neotoma from another database



• Recompilation into Neotoma from another database

• Compilation into Neotoma from primary source

• Recompilation into or revisions to Neotoma: The initial development of Neotoma involved merging thedata from several existing databases, including FAUNMAP, the Global Pollen Database, and the NorthAmerican Plant Macrofossil Database. Thus original compilation of Datasets was into one of thesedatabases, which were then recompiled into Neotoma. The original compilation and the recompilationinto Neotoma are separate submissions.

SQL Example

This query gives a list of Dataset Submissions for the site «Bear River No. 3» ordered by date.

1 SELECT DatasetTypes.DatasetType, Projects.ProjectName,2 DatasetSubmissions.SubmissionDate,3 DatasetSubmissionTypes.SubmissionType, DatasetSubmissions.Notes4

5 FROM Sites INNER JOIN (Projects INNER JOIN (DatasetTypes INNER JOIN6 (DatasetSubmissionTypes INNER JOIN ((CollectionUnits INNER JOIN Datasets7 ON CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) INNER8 JOIN DatasetSubmissions ON Datasets.DatasetID =9 DatasetSubmissions.DatasetID) ON DatasetSubmissionTypes.SubmissionTypeID

10 = DatasetSubmissions.SubmissionTypeID) ON DatasetTypes.DatasetTypeID =11 Datasets.DatasetTypeID) ON Projects.ProjectID =12 DatasetSubmissions.ProjectID) ON Sites.SiteID = CollectionUnits.SiteID13

14 WHERE (((Sites.SiteName)="Bear River No. 3"))15 ORDER BY DatasetSubmissions.SubmissionDate;

Result:

DatasetType Project-Name

Submis-sionDate

SubmissionType Notes

vertebratefauna

FAUN-MAP

1/31/1992 Compilation into a another relationaldatabase

vertebratefauna

Neotoma 11/24/2007 Compilation into Neotoma fromanother database

Compiled from FAUNMAP

mollusks Neotoma 11/25/2007 Compilation into Neotoma fromprimary source

plantmacrofossils

Neotoma 11/25/2007 Compilation into Neotoma fromprimary source

vertebratefauna

Neotoma 11/25/2007 Recompilation into or revisions toNeotoma

Bison elements, fish, andbirds added.

1.6.9 DatasetTypes

Lookup table for Dataset Types. Table is referenced by the Datasets table.

DatasetTypesDatasetTypeID Long Integer PKDatasetType Text

DatasetTypeID (Primary Key) An arbitrary Dataset Type identification number.

DatasetType The Dataset type, including the following:

• geochronologic



• loss-on-ignition

• pollen

• plant macrofossils

• vertebrate fauna

• mollusks

1.6.10 DatasetPIs

This table lists the Principle Investigators for Datasets.

DatasetPIsDatasetID Long Integer PK, FK DatasetsContactID Long Integer PK, FK ContactsPIOrder Long Integer

DatasetID (Primary Key, Foreign Key) Dataset identification number. Field links to Dataset table.

ContactID (Primary Key, Foreign Key) Contact identification number. Field links to Contacts table.

PIOrder Order in which PIs are listed.

1.6.11 DepEnvtTypes

Lookup table of Depostional Environment Types. Table is referenced by the CollectionUnits table.

DepEnvtTypesDepEnvtID Long Integer PKDepEnvt TextDepEnvtHigherID Long Integer FK DepEnvtTypes:DepEnvtID

DepEnvtID (Primary Key) An arbitrary Depositional Environment Type identification number.

DepEnvt Depositional Environment.

DepEnvtHigherID The Depositional Environment Types are hierarchical. DepEnvtHigherID is the DepEnvtID ofthe higher ranked Depositional Environment. See following table gives some examples.

DepEnvtID DepEnvt DepEnvtHigherID19 Lacustrine 1924 1929 Glacial 2430 2933 2959 Palustrine 5961 Mire 5962 Bog 6163 Blanket Bog 6264 Raised Bog 62

SQL Example

This query gives a list of the top level Depostional Environment Types.



1 SELECT DepEnvtTypes.DepEnvtID, DepEnvtTypes.DepEnvt,2 DepEnvtTypes.DepEnvtHigherID3

4 FROM DepEnvtTypes INNER JOIN DepEnvtTypes AS DepEnvtTypes\_1 ON5 (DepEnvtTypes.DepEnvt = DepEnvtTypes\_1.DepEnvt) AND6 (DepEnvtTypes.DepEnvtHigherID = DepEnvtTypes\_1.DepEnvtID);

Result:

DepEnvtID DepEnvt DepEnvtHigherID1 Archaeological 16 Biological 616 Estuarine 1619 Lacustrine 1951 Marine 5159 Palustrine 5976 Riverine 7693 Sampler 9399 Spring 99103 Terrestrial 103136 Other 136137 Unknown 137

SQL Example

This following query gives a list of the second level «Terrestrial» Depositional Environment Types.

1 SELECT DepEnvtTypes\_1.DepEnvtID, DepEnvtTypes\_1.DepEnvt,2 DepEnvtTypes\_1.DepEnvtHigherID3

4 FROM DepEnvtTypes INNER JOIN DepEnvtTypes AS DepEnvtTypes\_1 ON5 DepEnvtTypes.DepEnvtID = DepEnvtTypes\_1.DepEnvtHigherID6

7 WHERE (((DepEnvtTypes.DepEnvt)="Terrestrial"));

Result:

DepEnvtID DepEnvt DepEnvtHigherID103 Terrestrial 103104 Aeolian 103109 Cave 103117 Glacial 103122 Gravity 103127 Soil 103131 Volcanic 103

1.6.12 Lithology

This table stores the lithologic descriptions of Collection Units.



Table: LithologyLithologyID Long Integer PKCollectionUnitID Long Integer FK CollectionUnitsDepthTop DoubleDepthBottom DoubleLowerBoundary TextDescription Memo

LithologyID (Primary Key) An arbitrary identification number for a lithologic unit.

CollectionUnitID (Foreign Key) Collection Unit identification number. Field links to the CollectionUnits table.

DepthTop Depth of the top of the lithologic unit in cm.

DepthBottom Depth of the bottom of the lithologic unit in cm.

LowerBoundary Description of the nature of the lower boundary of the lithologic unit, e.g. «gradual, over ca. 10cm».

Description Description of the lithologic unit. These can be quite detailed, with Munsell color or Troels-Smithdescriptions. Some examples:

• interbedded gray silt and peat

• marly fine-detritus copropel

• humified sedge and Sphagnum peat

• sedge peat 5YR 5/4

• gray sandy loam with mammoth and other animal bones

• grey-green gyttja, oxidizing to gray-brown

• Ag 3, Ga 1, medium gray, firm, elastic

• nig3, strf0, elas2, sicc0; Th2 T12 Tb+

• Ld°4, , Dg+, Dh+

1.6.13 Projects

This table stores a list of database projects that have supplied data to Neotoma. These include the databases that weremerged in the initial development of Neotoma as well as other independent projects that continue to assemble datafor a particular region or data type. Some of these projects have developed relational databases, whereas others havecompiled data in flat files. This table is referenced by the DatasetSubmissions table.

Table: ProjectsProjectID Long Integer PKProjectName TextContactID Long Integer FK ContactsURL Text

ProjectID (Primary Key) An arbitrary Project identification number.

ProjectName Name of the Project, e.g. «Cooperative Holocene Mapping Project», «North American PollenDatabase», «FAUNMAP».

ContactID (Foreign Key) Contact person for the project. Field links to the Contacts table.

URL Web site address for the project.



1.7 Sample Related Tables

1.7.1 AnalysisUnits

This table stores the data for Analysis Units.

Column Name Type Key Table ReferenceAnalysisUnitID int PKCollectionUnitID int FK CollectionUnitsAnalysisUnitName nvarchar(80)Depth floatThickness floatFaciesID int FKMixed bitIGSN nvarchar(40)Notes ntext

AnalysisUnitID (Primary Key) An arbitrary Analysis Unit identification number.

CollectionUnitID (Foreign Key) Collection Unit ID number. Field links to CollectionUnits table. Every AnalysisUnit belongs to a Collection Unit.

AnalysisUnitName Optional name for an Analysis Unit. Analysis Units are usually designated with either a depth ora name, sometimes both.

Depth Optional depth of the Analysis Unit in cm. Depths are typically designated for Analysis Units from cores andfor Analysis Units excavated in arbitrary (e.g. 10 cm) levels. Depths are normally the midpoints of arbitrarylevels. For example, for a level excavated from 10 to 20 cm or for a core section from 10 to 15 cm, the depth is15. Designating depths as midpoints and thicknesses facilitates calculation of ages from age models that utilizesingle midpoint depths for Analysis Units rather than top and bottom depths. Of course, top and bottom depthscan be calculated from midpoint depths and thicknesses. For many microfossil core samples, only the midpointdepths are known or published; the diameter or width of the sampling device is often not given.

Thickness Optional thickness of the Analysis Unit in cm. For many microfossil core samples, the depths are treatedas points, and the thicknesses are not given in the publications, although 0.5 to 1.0 cm would be typical.

FaciesID Sedimentary facies of the Analysis Unit. Field links to the FaciesTypes table.

Mixed Indicates whether specimens in the Analysis Unit are of mixed ages, for example Pleistocene fossils occurringwith late Holocene fossils. Although Analysis Units may be mixed, samples from the Analysis Unit may notbe, for example individually radiocarbon dated specimens.

IGSN International Geo Sample Number. The IGSN is a unique identifier for a Geoscience sample. They are as-signed by the SESAR, the System for Earth Sample Registration (www.geosamples.org), which is a registry thatprovides and administers the unique identifiers. IGSN’s may be assigned to all types of geoscience samples, in-cluding cores, rocks, minerals, and even fluids. Their purpose is to facilitate sharing and correlation of samplesand sample-based data. For data in Neotoma, their primary value would be for correlation various samples fromthe same Analysis Units, for example pollen, charcoal, diatoms, and geochemical analyses. Conceivably, theAnalysisUnitID could be used for this purpose; however, IGSN’s could be assigned by projects before their dataare submitted to the database. Moreover, AnalysisUnitID’s are intended to be internal to the database. AlthoughIGSN’s could be assigned to Neotoma Collection Units and Samples, their primary value lies in their assign-ment to Analysis Units. IGSN’s are not yet assigned to Neotoma Analysis Units; however, that may changeafter consultation with SESAR.

Notes Free form notes or comments about the Analysis Unit.

1.7. Sample Related Tables 39

http://www.geosamples.org


1.7.2 Data

The primary data table in the database. Each occurrence of a Variable in a sample comprises a record in the Data table.

Table: DataSampleID Long Integer PK, FK SamplesVariableID Long Integer PK, FK VariablesValue Double

SampleID (Primary Key, Foreign Key) Sample identification number. Field links to Samples table.

VariableID (Primary Key, Foreign Key) Variable identification number. Field links to Variables table.

Value The value of the variable.

SQL Example

The following SQL example gives a list of vertebrate taxa by Analysis Unit for all sites. Also listed are VariableMeasurement Units and Values.

1 SELECT2 AnalysisUnits.AnalysisUnitName,3 Taxa.TaxonName,4 VariableUnits.VariableUnits,5 `Data.Value`6

7 FROM8 VariableUnits9 INNER JOIN (

10 AnalysisUnits11 INNER JOIN (12 DatasetTypes13 INNER JOIN (14 Taxa15 INNER JOIN (16 `Variables`17 INNER JOIN (18 (19 (20 (21 Sites22 INNER JOIN CollectionUnits ON Sites.SiteID = CollectionUnits.SiteID23 )24 INNER JOIN Datasets ON CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID25 )26 INNER JOIN Samples ON Datasets.DatasetID = Samples.DatasetID27 )28 INNER JOIN `Data` ON Samples.SampleID = `Data.SampleID`29 ) ON `Variables.VariableID` = `Data.VariableID`30 ) ON Taxa.TaxonID = `Variables.TaxonID`31 ) ON DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID32 AND (DatasetTypes.DatasetType) = "vertebrate fauna"33 ) ON (34 CollectionUnits.CollectionUnitID = AnalysisUnits.CollectionUnitID35 )36 AND (37 AnalysisUnits.AnalysisUnitID = Samples.AnalysisUnitID38 )39 ) ON VariableUnits.VariableUnitsID = `Variables.VariableUnitsID`



40

41 LIMIT 5;

The first few lines of the result:

AnalysisUnitName TaxonName VariableUnits ValueLevel 10 prov. 1-3 Clethrionomys gapperi NISP 2Level 10 prov. 1-3 Cricetidae NISP 29Level 10 prov. 1-3 Dicrostonyx torquatus NISP 5Level 10 prov. 1-3 Lemmus sibiricus NISP 12Level 10 prov. 1-3 Marmota caligata NISP 38Level 10 prov. 1-3 Martes NISP 2

1.7.3 DepAgents

Deposition Agents for Analysis Units. Individual Analysis Units may be listed multiple times with different DepositionAgents.

DepAgentsAnalysisUnitID Long Integer PK, FK AnalysisUnitsDepAgentID Long Integer PK, FK DepAgentTypes

AnalysisUnitID (Primary Key) Analysis Unit identification number. Field links to AnalysisUnits table.

DepAgentID Deposition Agent identification number. Field links to DepAgentTypes table.

1.7.4 DepAgentTypes

Lookup table of Depositional Agents. Table is referenced by the DepAgents table.

DepAgentTypesDepAgentID Long Integer PKDepAgent Text

DepAgentID (Primary Key) An arbitrary Depositional Agent identification number.

DepAgent Depostional Agent.

1.7.5 SampleAges

This table stores sample ages. Ages are assigned to a Chronology. Because there may be more than one Chronologyfor a Collection Unit, samples may be assigned different ages for different Chronologies. A simple example is onesample age in radiocarbon years and another in calibrated radiocarbon years. The age units are an attribute of theChronology.

Table: SampleAgesSampleAgeID Long Integer PKSampleID Long Integer FK SamplesChronologyID Long Integer FK ChronologiesAge DoubleAgeYounger DoubleAgeOlder Double

SampleAgeID (Primary Key) An arbitrary Sample Age identification number.

SampleID (Foreign Key) Sample identification number. Field links to the Samples table.



ChronologyID (Foreign Key) Chronology identification number. Field links to the Chronologies table.

Age Age of the sample

AgeYounger Younger error estimate of the age. The definition of this estimate is an attribute of the Chronology.Many ages do not have explicit error estimates assigned.

AgeOlder Older error estimate of the age.

SQL Example

This query lists the Sample Ages for the default Chronologies for «». The CollectionUnit.Handle indicates that thereis only one Collection Unit from this site. There are two default Chronologies, one in «Radiocarbon years BP» andthe other in «Calibrated radiocarbon years BP».

1 SELECT2 s.SiteName,3 -- From the Sites table4 c.Handle,5 -- From the CollectionUnits table6 sa.Age,7 -- From the SampleAges table8 a.AgeType -- From the AgeTypes table9 FROM

10 AgeTypes AS a11 INNER JOIN (12 (13 (14 Sites AS s15 INNER JOIN CollectionUnits AS c ON s.SiteID = c.SiteID16 )17 INNER JOIN Chronologies AS ch ON c.CollectionUnitID = ch.CollectionUnitID18 )19 INNER JOIN SampleAges AS sa ON ch.ChronologyID = sa.ChronologyID20 ) ON a.AgeTypeId = ch.AgeTypeID21 WHERE22 (s.SiteName) = "Muskox Lake"23 AND (ch.IsDefault) = TRUE;

The first five lines of the result for each Age Type:

SiteName Handle Age AgeTypeMUSKOX -50 Radiocarbon years BPMUSKOX 538 Radiocarbon years BPMUSKOX 1125 Radiocarbon years BPMUSKOX 1712 Radiocarbon years BPMUSKOX 2300 Radiocarbon years BPMUSKOX -50 Calibrated radiocarbon years BPMUSKOX 604 Calibrated radiocarbon years BPMUSKOX 1258 Calibrated radiocarbon years BPMUSKOX 1912 Calibrated radiocarbon years BPMUSKOX 2567 Calibrated radiocarbon years BP

1.7.6 SampleAnalysts

This table lists the Sample Analysts.



Table: SampleAnalystsAnalystID Long Integer PKSampleID Long Integer FK SamplesContactID Long Integer FK ContactsAnalystOrder Long Integer

AnalystID (Primary Key) An arbitrary Sample Analyst identification number.

SampleID (Foreign Key) Sample identification number. Field links to the Samples table.

ContactID (Foreign Key) Contact identification number. Field links to the Contacts table.

AnalystOrder Order in which Sample Analysts are listed if more than one (rare).

1.7.7 SampleKeywords

This table links keywords to samples. For example, it identifies modern pollen surface samples.

Table: SampleKeywordsSampleID Long Integer PK, FK SamplesKeywordID Long Integer PK, FK Keywords

SampleID (Primary Key, Foreign Key) Sample identification number. Field links to the Samples table.

KeywordID (Primary Key, Foreign Key) Keyword identification number. Field links to the Keywords lookup table.

SQL Example

This query provides a list of modern pollen surface samples from «». Listed are the Site Name, Collection Type,Contact person, and Depositional Environment.

1 SELECT Samples.SampleID, Sites.SiteName, CollectionTypes.CollType,2 Contacts.ContactName, DepEnvtTypes.DepEnvt3

4 FROM DepEnvtTypes INNER JOIN (Contacts INNER JOIN ((CollectionTypes5 INNER JOIN (GeoPoliticalUnits INNER JOIN ((Sites INNER JOIN6 (CollectionUnits INNER JOIN (DatasetTypes INNER JOIN (Datasets INNER7 JOIN (Samples INNER JOIN (Keywords INNER JOIN SampleKeywords ON8 Keywords.KeywordID = SampleKeywords.KeywordID) ON Samples.SampleID =9 SampleKeywords.SampleID) ON Datasets.DatasetID = Samples.DatasetID) ON

10 DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID) ON11 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) ON12 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN SiteGeoPolitical ON13 Sites.SiteID = SiteGeoPolitical.SiteID) ON14 GeoPoliticalUnits.GeoPoliticalID = SiteGeoPolitical.GeoPoliticalID) ON15 CollectionTypes.CollTypeID = CollectionUnits.CollTypeID) INNER JOIN16 DatasetPIs ON Datasets.DatasetID = DatasetPIs.DatasetID) ON17 Contacts.ContactID = DatasetPIs.ContactID) ON DepEnvtTypes.DepEnvtID =18 CollectionUnits.DepEnvtID19

20 WHERE (((Keywords.Keyword)="modern sample") AND21 ((DatasetTypes.DatasetType)="pollen") AND22 ((GeoPoliticalUnits.GeoPoliticalName)=""))23

24 ORDER BY CollectionTypes.CollType;

Result:



Sam-pleID

SiteName Coll-Type

ContactName DepEnvt

60536 Core Radle, Nancy Jean Glacial11153 (US:) Core Grimm, Eric

ChristopherGlacial

61194 (US:) Core Watts, William A. Glacial24780 JHMS31 (McAndrews and Wright

1969)Modern McAndrews, John H. Organic

Detritus24771 JHMS23 (McAndrews and Wright













1969)Modern McAndrews, John H. Cattle Tank

3173 Site 1 (Hansen unpublished) Modern Hansen, Barbara C. S. Unknown24779 JHMS30 (McAndrews and Wright





1969)Modern McAndrews, John H. Cattle Tank

24783 JHMS33 (McAndrews and Wright1969)

Modern McAndrews, John H. OrganicDetritus

45068 K11 (Kapp 1965] Modern Kapp, Ronald O. Stock Pond55819 Rose 1a (Watts and Wright 1966) Modern Watts, William A. Stock Pond55819 Rose 1a (Watts and Wright 1966) Modern Wright, Herbert E., Jr. Stock Pond55820 Rose 1b (Watts and Wright 1966) Modern Watts, William A. Stock Pond55820 Rose 1b (Watts and Wright 1966) Modern Wright, Herbert E., Jr. Stock Pond24778 JHMS29 (McAndrews and Wright


Detritus

1.7.8 Samples

This table stores sample data. Samples belong to Analysis Units, which belong to Collection Units, which belong toSites. Samples also belong to a Dataset, and the Dataset determines the type of sample. Thus, there could be twodifferent samples from the same Analysis Unit, one belonging to a pollen dataset, the other to a plant macrofossildataset.



Table: SamplesSampleID Long Integer PKAnalysisUnitID Long Integer FK AnalysisUnitsDatasetID Long Integer FK DatasetsSampleName TextAnalysisDate Date/TimeLabNumber TextPreparationMethod MemoNotes Memo

SampleID (Primary Key) An arbitrary Sample identification number.

AnalysisUnitID (Foreign Key) Analysis Unit identification number. Field links to the AnalysisUnits table.

DatasetID (Foreign Key) Dataset identification number. Field links to the Datasets table.

SampleName Sample name if any.

AnalysisDate Date of analysis.

LabNumber Laboratory number for the sample. A special case regards geochronologic samples, for which the Lab-Number is the number, if any, assigned by the submitter, not the number assigned by the radiocarbon laboratory,which is in the Geochronology table.

PreparationMethod Description, notes, or comments on preparation methods. For faunal samples, notes on screen-ing methods or screen size are stored here.

Notes Free form note or comments about the sample.

SQL Example

This query provides a list of samples from «». The Collection Unit Name, Analysis Unit Name, Dataset Type, andPreparation Methods are listed.

1 SELECT CollectionUnits.CollUnitName, AnalysisUnits.AnalysisUnitName,2 DatasetTypes.DatasetType, Samples.PreparationMethod3

4 FROM DatasetTypes INNER JOIN ((((Sites INNER JOIN CollectionUnits ON5 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN AnalysisUnits ON6 CollectionUnits.CollectionUnitID = AnalysisUnits.CollectionUnitID) INNER7 JOIN Samples ON AnalysisUnits.AnalysisUnitID = Samples.AnalysisUnitID)8 INNER JOIN (Datasets.DatasetID = Samples.DatasetID) AND9 (CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID)) ON

10 DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID11

12 WHERE (((Sites.SiteName)=""))13

14 ORDER BY CollectionUnits.CollUnitName, AnalysisUnits.AnalysisUnitName;

Result:



CollUnit-Name

AnalysisUnitName Dataset-Type

PreparationMethod

Locality Assemblage vertebratefauna

Locality Large MammalAssemblage

vertebratefauna

Unit A Level 1 vertebratefauna

All excavated material was fine screened (<1/16-inch or1.6-mm mesh).

Unit A Level 1 geochrono-logic





Unit A Level 2 vertebratefauna





Unit A/B Assemblage vertebratefauna


Unit B Level 4 vertebratefauna


Unit B Level 5 vertebratefauna


Unit C Level 1 vertebratefauna


Unit C Level 1 geochrono-logic

Unit C Level 2 vertebratefauna





1.7.9 AggregateSamples

This table stores the samples in Aggregate Datasets.

AggregateSamplesAggregateDatasetID int PK, FK AggregateDatasetsSampleID int PK, FK Samples



AggregateDatasetID (Primary Key, Foreign Key) An arbitrary Aggregate Dataset identification number. Fieldlinks to the AggregateDatasets table.

SampleID (Primary Key, Foreign Key) Sample ID number. Field links to the Samples table.

1.7.10 FaciesTypes

Lookup table of Facies Types. Table is referenced by the AnalysisUnits table.

FaciesTypesFaciesID Long Integer PKFacies Text

FaciesID (Primary Key) An arbitrary Facies identification number.

Facies Short Facies description.

1.7.11 Keywords

Lookup table of Keywords referenced by the SampleKeywords table. The table provides a means to identify samplessharing a common attribute. For example, the keyword «modern sample» identifies modern surface samples in thedatabase. These samples include individual surface samples, as well as core tops. Although not implemented, a«pre-European settlement» keyword would be a means to identify samples just predating European settlement.

Table: KeywordsKeywordID Long Integer PKKeyword Text

KeywordID (Primary Key) An arbitrary Keyword identification number.

Keyword A keyword for identifying samples sharing a common attribute.

1.8 Site Related Tables

1.8.1 SiteImages

This table stores hyperlinks to jpeg images of sites.

SiteImagesSiteImageID Long Integer PKSiteID Long Integer FK SitesContactID Long Integer FK ContactsCaption MemoCredit TextDate Date/TimeSiteImage Hyperlink

SiteImageID (Primary Key) An arbitrary Site Image identification number.

SiteID (Foreign Key) Site identification number. Field links to the Sites table.

ContactID (Foreign Key) Contact identification number for image attribution from the Contacts table.

Caption Caption for the image.

Credit Credit for the image. If null, the credit is formed from the ContactID.

1.8. Site Related Tables 47


Date Date of photograph or image.

SiteImage Hyperlink to a URL for the image.

1.8.2 Sites

The Sites table stores information about sites or localities, including name, geographic coordinates, and description.Sites generally have an areal extent and can be circumscribed by a latitude-longitude box. However, site data ingestedfrom legacy databases have included only point locations. The lat-long box can be used either to circumscribe the arealextent of a site or to provide purposeful imprecision to the site location. Site location may be imprecise because of theoriginal description was vague, e.g. «a gravel bar 5 miles east of town», or because the investigators, land owner, orland management agency may not want the exact location made public, perhaps to prevent looting and vandalism. Inthe first case, the lat-long box can be made sufficiently large to encompass the true location and in the second case toprevent exact location.

SitesSiteID Long Integer PKSiteName TextLongitudeEast DoubleLatitudeNorth DoubleLongitudeWest DoubleLatitudeSouth DoubleAltitude Long IntegerArea DoubleSiteDescription MemoNotes Memo

SiteID (Primary Key) An arbitrary Site identification number.

SiteName Name of the site. Alternative names, including archaeological site numbers, are placed in square brackets,for example:

• New #4 [Lloyd’s Rock Hole]

• Modoc Rock Shelter [11RA501]

A search of the SiteName field for any of the alternative names or for the archaeological site number will findthe site. Some archaeological sites are known only by their site number.

Modifiers to site names are placed in parentheses. Authors are added for generic sites names, especially forsurface samples, that are duplicated in the database, for example:

• Site 1 (Heusser 1978)

• Site 1 (Delcourt et al. 1983)

• Site 1 (Elliot-Fisk et al. 1982)

• Site 1 (Whitehead and Jackson 1990)

For actual site names duplicated in the database, the name is followed by the 2-letter country code and state or province, for example:

• (US:)

• (CA:)

• (US:)

• (US:)

LongitudeEast East bounding longitude for a site.



LatitudeNorth North bounding latitude for a site.

LongitudeWest West bounding longitude for a site.

LatitudeSouth South bounding latitude for a site.

Altitude Altitude of a site in meters.

Area Area of a site in hectares.

SiteDescription Free form description of a site, including such information as physiography and vegetation aroundthe site.

Notes Free form notes or comments about the site.

1.8.3 SiteGeoPolitical

This table lists the GeoPolitical units in which sites occur.

SiteGeoPoliticalSiteGeoPoliticalID Long Integer PKSiteID Long Integer FK SitesGeoPoliticalID Long Integer FK GeoPoliticalUnits

SiteGeoPoliticalID (Primary Key) An arbitrary Site GeoPolitical identification number.

SiteID (Foreign Key) Site identification number. Field links to the Sites table.

GeoPoliticalID (Foreign Key) GeoPolitical identification number. Field links to the GeoPoliticalUnits lookup table.

SQL Example

The query in Example 2.8.1 lists the GeoPoliticalUnits for «», one unit to a record. This query lists them in a singlerecord.

1 SELECT Sites.SiteName, GeoPoliticalUnits.GeoPoliticalName,2 GeoPoliticalUnits\_1.GeoPoliticalName,3 GeoPoliticalUnits\_2.GeoPoliticalName4

5 FROM GeoPoliticalUnits AS GeoPoliticalUnits\_2 INNER JOIN6 (SiteGeoPolitical AS SiteGeoPolitical\_2 INNER JOIN ((SiteGeoPolitical7 AS SiteGeoPolitical\_1 INNER JOIN (GeoPoliticalUnits INNER JOIN (Sites8 INNER JOIN SiteGeoPolitical ON Sites.SiteID = SiteGeoPolitical.SiteID)9 ON GeoPoliticalUnits.GeoPoliticalID = SiteGeoPolitical.GeoPoliticalID)

10 ON SiteGeoPolitical\_1.SiteID = Sites.SiteID) INNER JOIN11 GeoPoliticalUnits AS GeoPoliticalUnits\_1 ON12 SiteGeoPolitical\_1.GeoPoliticalID =13 GeoPoliticalUnits\_1.GeoPoliticalID) ON SiteGeoPolitical\_2.SiteID =14 Sites.SiteID) ON GeoPoliticalUnits\_2.GeoPoliticalID =15 SiteGeoPolitical\_2.GeoPoliticalID16

17 WHERE (((Sites.SiteName)="") AND ((GeoPoliticalUnits.Rank)=1) AND18 ((GeoPoliticalUnits\_1.Rank)=2) AND ((GeoPoliticalUnits\_2.Rank)=3));

Result:

Site-Name

GeoPoliticalU-nits.GeoPoliticalName

GeoPoliticalU-nits_1.GeoPoliticalName

GeoPoliticalU-nits_2.GeoPoliticalNameHennepin



SQL Example

The problem with the query above is that if a site has less than three GeoPolitical Names, the result will return empty.For example, «» has no GeoPoliticalUnit with Rank = 3, and will return an empty result with the above query. Asolution to this problem is to create and save separate queries for the three ranks:

Query GeoPol1:

1 SELECT Sites.SiteName, GeoPoliticalUnits.GeoPoliticalName2

3 FROM Sites INNER JOIN (GeoPoliticalUnits INNER JOIN SiteGeoPolitical ON4 GeoPoliticalUnits.GeoPoliticalID = SiteGeoPolitical.GeoPoliticalID) ON5 Sites.SiteID = SiteGeoPolitical.SiteID6

7 WHERE (((GeoPoliticalUnits.Rank)=1));

Query GeoPol2:




Query GeoPol3:




These three queries can now be combined in a new query with left joins, and the GeoPolitical Names will be returnedeven if there are less than three.

1 SELECT GeoPol1.SiteName, GeoPol1.GeoPoliticalName,2 GeoPol2.GeoPoliticalName, GeoPol3.GeoPoliticalName3

4 FROM (GeoPol1 LEFT JOIN GeoPol2 ON GeoPol1.SiteName = GeoPol2.SiteName)5 LEFT JOIN GeoPol3 ON GeoPol2.SiteName = GeoPol3.SiteName6

7 WHERE (((GeoPol1.SiteName)="Lofty "));

Result:

SiteName GeoPol1.GeoPoliticalName GeoPol2.GeoPoliticalName GeoPol3.GeoPoliticalNameLofty

SQL Example

The saved queries from the example above can be linked with tables in a more complicated query. This query lists allthe pollen sites in the adjacent states of «» in the «» and «» in «».

1 SELECT GeoPol1.SiteName, GeoPol1.GeoPoliticalName,2 GeoPol2.GeoPoliticalName, GeoPol3.GeoPoliticalName,



3 DatasetTypes.DatasetType4

5 FROM (DatasetTypes INNER JOIN ((Sites INNER JOIN CollectionUnits ON6 Sites.SiteID = CollectionUnits.SiteID) INNER JOIN Datasets ON7 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) ON8 DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID) INNER JOIN9 ((GeoPol1 LEFT JOIN GeoPol2 ON GeoPol1.SiteName = GeoPol2.SiteName) LEFT

10 JOIN GeoPol3 ON GeoPol2.SiteName = GeoPol3.SiteName) ON Sites.SiteName =11 GeoPol1.SiteName12

13 GROUP BY GeoPol1.SiteName, GeoPol1.GeoPoliticalName,14 GeoPol2.GeoPoliticalName, GeoPol3.GeoPoliticalName,15 DatasetTypes.DatasetType16

17 HAVING (((GeoPol1.GeoPoliticalName)="" Or (GeoPol1.GeoPoliticalName)="")18 AND ((GeoPol2.GeoPoliticalName)="" Or (GeoPol2.GeoPoliticalName)="") AND19 ((DatasetTypes.DatasetType)="pollen"))20

21 ORDER BY GeoPol1.GeoPoliticalName, GeoPol2.GeoPoliticalName,22 GeoPol3.GeoPoliticalName, DatasetTypes.DatasetType;

Result:

SiteName GeoPol1.GeoPoliticalNameGeoPol2.GeoPoliticalNameGeoPol3.GeoPoliticalNameDataset-Type

Sierra Bacha pollenSierra Bacha 3 pollen

Apache pollenCoconino pollenCoconino pollenCoconino pollen

MontezumaWell

Yavapai pollen

1.8.4 GeoPoliticalUnits

Lookup table of GeoPoliticalUnits. Table is referenced by the SiteGeoPolitical table. These are countries and varioussubdivisions. Countries and subdivisions were acquired from the U.S. Central Intelligence Agency World Factbook 8

and the ISO 3166-1 and ISO 3166-2 databases 9.

Each GeoPolitical Unit has a rank. GeoPolitical Units with Rank 1 are generally countries. There are a few exceptions,including Antarctica and island territories, such as , which although a Danish territory, is geographically separateand distinct. Rank 2 units are generally secondary political divisions with various designations: e.g. states in the, provinces in , and regions in . For some countries, the secondary divisions are not political but rather distinctgeographic entities, such as islands. The secondary divisions of some island nations include either groups of islandsor sections of more highly populated islands; however, the actual island on which a site is located is more importantinformation. Some countries also have Rank 3 units, e.g. counties in the and metropolitan departments in . In additionto purely political units, various other administrative regions and geographic entities can be contained in this table.Examples of administrative regions are National Parks and Forests. It might be quite useful, for example, to have arecord of all the sites in . These additional units are Rank 4, and they can be added to the database as warranted.

8 https://www.cia.gov/library/publications/the-world-factbook/9 http://www.iso.org/iso/country_codes/iso_3166_databases.htm


https://www.cia.gov/library/publications/the-world-factbook/

http://www.iso.org/iso/country_codes/iso_3166_databases.htm


Table: GeoPoliticalUnitsGeoPoliticalID Long Integer PKGeoPoliticalName TextGeoPoliticalUnit TextRank Long IntegerHigherGeoPoliticalID Long Integer FK GeoPoliticalUnits:GeoPoliticalID

GeoPoliticalID (Primary Key) An arbitrary GeoPolitical identification number.

GeoPoliticalName Name of the GeoPolitical Unit, e.g. , .

GeoPoliticalUnit The name of the unit, e.g. country, state, county, island, governorate, oblast.

Rank The rank of the unit.

HigherGeoPoliticalID The GeoPoliticalUnit with higher rank, e.g. the country in which a state lies.

SQL Example

The following query lists all the political subdivisions of in the .

1 SELECT GeoPoliticalUnits\_2.Rank, GeoPoliticalUnits\_2.GeoPoliticalUnit,2 GeoPoliticalUnits\_2.GeoPoliticalName3

4 FROM (GeoPoliticalUnits AS GeoPoliticalUnits\_2 RIGHT JOIN5 (GeoPoliticalUnits AS GeoPoliticalUnits\_1 RIGHT JOIN GeoPoliticalUnits6 ON GeoPoliticalUnits\_1.HigherGeoPoliticalID =7 GeoPoliticalUnits.GeoPoliticalID) ON8 GeoPoliticalUnits\_2.HigherGeoPoliticalID =9 GeoPoliticalUnits\_1.GeoPoliticalID) LEFT JOIN GeoPoliticalUnits AS

10 GeoPoliticalUnits\_3 ON GeoPoliticalUnits\_2.GeoPoliticalID =11 GeoPoliticalUnits\_3.HigherGeoPoliticalID12

13 WHERE (((GeoPoliticalUnits.GeoPoliticalName)="") AND14 ((GeoPoliticalUnits\_1.GeoPoliticalName)=""));

The first 17 records of the result:

Rank GeoPoliticalUnit GeoPoliticalName3 district Omagh3 district North Down3 district Strabane3 district Newry and Mourne3 district Moyle3 district Magherafelt3 district4 historical county4 historical county4 historical county4 historical county4 historical county3 district Banbridge3 district Lisburn4 historical county3 district Ballymoney3 district Carrickfergus



1.9 Taxonomy Related Tables

1.9.1 EcolGroups

Taxa are assigned to Sets of Ecological Groups. A taxon may be assigned to more than one Set of Ecological Groups,representing different schemes for organizing taxa.

EcolGroupsTaxonID Long Integer PK, FK TaxaEcolSetID Long Integer PK, FK EcolSetTypesEcolGroupID Text FK EcolGroupTypes

TaxonID (Primary Key, Foreign Key) Taxon identification number. The field links to the Taxa table.

EcolSetID (Primary Key, Foreign Key) Ecological Set identification number. Field links to the EcolSetTypes table.

EcolGroupID (Foreign Key) A four-letter Ecological Group identification code. Field links to the EcolGroupTypestable.

SQL Example

The following query produces a list of Ecological Groups for vascular plants (VPL).

1 SELECT2 Taxa.TaxaGroupID,3 EcolGroups.EcolGroupID,4 EcolSetTypes.EcolSetName,5 EcolGroupTypes.EcolGroup6 FROM7 EcolSetTypes8 INNER JOIN (9 EcolGroupTypes

10 INNER JOIN (11 Taxa12 INNER JOIN EcolGroups ON Taxa.TaxonID = EcolGroups.TaxonID13 ) ON EcolGroupTypes.EcolGroupID = EcolGroups.EcolGroupID14 ) ON EcolSetTypes.EcolSetID = EcolGroups.EcolSetID15 GROUP BY16 Taxa.TaxaGroupID,17 EcolGroups.EcolGroupID,18 EcolSetTypes.EcolSetName,19 EcolGroupTypes.EcolGroup20 HAVING21 (((Taxa.TaxaGroupID) = "VPL"));

Result:

TaxaGroupID EcolGroupID EcolSetName EcolGroupVPL ANAC Default plant AnachronicVPL AQVP Default plant Aquatic Vascular PlantsVPL TRSH Default plant Trees and ShrubsVPL UNID Default plant Unknown and IndeterminableVPL UPHE Default plant Upland HerbsVPL VACR Default plant Terrestrial Vascular Cryptogams

1.9. Taxonomy Related Tables 53


SQL Example

This query lists all the taxa in the Ecological Group «Sirenia».

1 SELECT2 EcolGroupTypes.EcolGroup,3 Taxa.TaxonName4 FROM5 EcolGroupTypes6 INNER JOIN (7 Taxa8 INNER JOIN EcolGroups ON Taxa.TaxonID = EcolGroups.TaxonID9 ) ON EcolGroupTypes.EcolGroupID = EcolGroups.EcolGroupID

10 WHERE11 (12 (13 (EcolGroupTypes.EcolGroup) = "Sirenia"14 )15 );

Result:

EcolGroup TaxonNameSirenia DugongidaeSirenia Hydrodamalis gigasSirenia SireniaSirenia TrichechidaeSirenia Trichechus manatusSirenia HydrodamalisSirenia Trichechus

1.9.2 EcolGroupTypes

Lookup table of Ecological Group Types. The table is referenced by the EcolGroups table.

EcolGroupTypesEcolGroupID Text PKEcolGroup Text

EcolGroupID (Primary Key) An arbitrary Ecological Group identification number.

EcolGroup Ecological Group.

1.9.3 EcolSetTypes

Lookup table of Ecological Set Types. The table is referenced by the EcolGroups table.

EcolSetTypesEcolSetID Long Integer PKEcolSetName Text

EcolSetID (Primary Key) An arbitrary Ecological Set identification number.

EcolSetName The Ecological Set name.



1.9.4 Synonyms

This table lists common synonyms for taxa in the Taxa table. No effort has been made to provide a complete taxonomicsynonymy, but rather to list synonyms commonly used in recent literature.

Table: SynonymsSynonymID Long Integer PKSynonymName TextTaxonID Long Integer FK TaxaPublicationID Long Integer FK PublicationsSynonymTypeID Long Integer FK SynonymTypesNotes Memo

SynonymID (Primary Key) An arbitrary synonym identification number.

SynonymName Name of the synonym.

TaxonID (Foreign Key) The accepted taxon name in Neotoma. This field links to Taxa table.

PublicationID (Foreign Key) Published authority for synonymy. Field links to Publications table.

SynonymTypeID (Foreign Key) Type of synonym. Field links to the SynonymTypes lookup table.

Notes Free form notes or comments about the synonymy.

1.9.5 SynonymTypes

Lookup table of Synonym Types. Table is referenced by the Synonyms table.

Table: SynonymTypesSynonymTypeID Long Integer PKSynonymType Text

SynonymTypeID (Primary Key) An arbitrary Synonym Type identification number.

SynonymType Synonym type. Below are some examples:

nomenclatural, homotypic, or objective synonym a synonym that unambiguously refers to the same taxon,particularly one with the same description or type specimen. These synonyms are particularly commonabove the species level. For example, Gramineae = Poaceae, Clethrionomys gapperi = Myodes gapperi.The term «objective» is used in zoology, whereas «nomenclatural» or «homotypic» is used in botany.

taxonomic, heterotypic, or subjective synonym a synonym typically based on a different type specimen, butwhich is now regarded as the same taxon as the senior synonym. For example, Iva ciliata = Iva annua.The term «subjective» is used in zoology, whereas «taxonomic» or «heterotypic» is used in botany.

genus merged into another genus heterotypic or subjective synonym; a genus has been merged into anothergenus and has not been retained at a subgeneric rank. This synonymy may apply to either the generic orspecific level, for example: Petalostemon = Dalea, Petalostemon purpureus = Dalea purpurea.

family merged into another family Heterotypic or subjective synonym; a family has been merged into an-other family and has not been retained at a subfamilial rank. For example, the Taxodiaceae has beenmerged with the Cupressaceae. This synonymy creates issues for data entry, because palynologically theTaxodiaceae sensu stricto is sometimes distinguishable from the Cupressaceae sensu stricto. If a pollentype was identified as «Cupressaceae/Taxodiaceae», then synonymizing to «Cupressaceae» results in noloss of information. However, synonymizing «Taxodiaceae» to «Cupressaceae» potentially does. In thiscase, consultation with the original literature or knowledge of the local biogeography may point to a logicalname change that will retain the precision of the original identification. For example, in the southeastern ,«Taxodiaceae» can be changed to «Taxodium» or «Taxodium-type» in most situations. If «Cupressaceae»



was also identified, then it should be changed to «Cupressaceae undiff.» or possibly «Juniperus-type» ifother Cupressaceae such as Chamaecyperus are unlikely.

rank change: species reduced to subspecific rank heterotypic or subjective synonym; a species has been re-duced to a subspecies or variety of another species. These synonyms may be treated in two different ways,depending on the situation or protocols of the contributing data cooperative: (1) The taxon is reduced tothe subspecific rank (e.g. Alnus fruticosa = Alnus viridis subsp. fruticosa, Canis familiaris = Canis lu-pus familiaris), either because the fossils can be assigned to the subspecies based on morphology, as islikely the case with the domestic dog, Canis lupus familiaris, or because the subspecies can be assignedconfidently based on biogeography. (2) The taxon is changed to the new taxon and the subspecific rankis dropped because the fossil is not distinguishable at the subspecific level. For example, Alnus rugosa =Alnus incana subsp. rugosa, but may simply be changed to Alnus incana because the pollen of A. incanasubsp. rugosa and A. incana subsp. incana are indistinguishable morphologically.

rank change: genus reduced to subgenus heterotypic or subjective synonym; a genus has been reduced tosubgeneric rank in another family. At the generic level, this synonymy is clear from the naming con-ventions, e.g. Mictomys = Synaptomys (Mictomys); however, at the species level it is not, e.g. Mictomysborealis = Synaptomys borealis.

rank change: family reduced to subfamily heterotypic or subjective synonym; a family has been reduced tosubfamily rank in another family. By botanical convention the family name is retained, e.g. Pyrolaceae= Ericaceae subf. Monotropoideae; whereas by zoological convention it is not, e.g. Desmodontidae =Desmodontinae.

rank change: subspecific rank elevated to species heterotypic or subjective synonym; a subspecies or varietyhas been raised to the species rank, e.g. Ephedra fragilis subsp. campylopoda = Ephedra foeminea.

rank change: subgeneric rank elevated to genus heterotypic or subjective synonym; a subgenus or other sub-generic rank has been raised to the generic rank. At the subgeneric level, this synonymy is clear from thenaming conventions, e.g. Potamogeton subg. Coleogeton = Stuckenia; however, at the species level it isnot, e.g. Potamogeton pectinatus = Stuckenia pectinata.

rank change: subfamily elevated to family heterotypic or subjective synonym; a subfamily has been raisedto the family rank, e.g. Liliaceae subf. Amaryllidoideae = Amaryllidaceae, Pampatheriinae = Pampatheri-idae.

rank elevated because of taxonomic uncertainty because the precise taxonomic identification is uncertain,the rank has been raised to a level that includes the universe of possible taxa. A common cause of suchuncertainty is taxonomic splitting subsequent to the original identification, in which case the originallyidentified taxon is now a much smaller group. For example, the genus Psoralea has been divided intoseveral genera; the genus Psoralea still exists, but now includes a much smaller number of species. Con-sequently, in the database Psoralea has been synonymized with Fabaceae tribe Psoraleeae, which includesthe former Psoralea sensu lato. A zoological example is Mustela sp. The genus Mustela formerly in-cluded the minks, which have now been separated into the genus Neovison. Consequently, Mustela sp. =Mustela/Neovison sp.

globally monospecific genus Although identified at the genus level, specimens assigned to this genus can befurther assigned to the species level because the genus is monospecific.

globally monogeneric family although identified at the family level, specimens assigned to this family can befurther assigned to the genus level because the family is monogeneric.

SQL Example

This query provides the preferred synonym in the database for «Bison alleni» along with the published authority forthe synonymy and the notes in the database on the rationale for the synonymy. The notes indicate some potentialproblems with this synonymy.



1 SELECT Synonyms.SynonymName, Taxa.TaxonName, Publications.Citation,2 Synonyms.Notes3

4 FROM Publications INNER JOIN (Taxa INNER JOIN Synonyms ON Taxa.TaxonID =5 Synonyms.TaxonID) ON Publications.PublicationID = Synonyms.PublicationID6

7 WHERE (((Synonyms.SynonymName)="Bison alleni"));

Result:

Syn-onymName

Tax-on-Name

Citation Notes

Bisonalleni

Bi-sonlat-ifrons

McDonald, J. N. 1981. NorthAmerican bison: theirclassification and evolution. ofPress, .

According to MacDonald (1981, p. 73), the holotype of B.alleni is clearly consistent with B. latifrons; however, henotes that many specimens identified as B. alleni have beenconfused with B. alaskensis (=priscus), a situation whichmay relegate B. alleni to a nomen dubium. (1974)synonymized B. alleni with B. priscus. He also consideredB. latifrons and B. alaskensis to be subspecies of B. priscus.[ECG, 3 Aug 2007].

1.9.6 Taxa

This table lists all taxa in the database. Most taxa are biological taxa; however, some are biometric measures and someare physical parameters.

Table: TaxaTaxonID Long Integer PKTaxonCode TextTaxonName TextAuthor TextHigherTaxonID Long Integer Taxa:TaxonIDExtinct Yes/NoTaxaGroupID Text FK TaxaGroupTypesPublicationID Long Integer FK PublicationsNotes Memo

TaxonID (Primary Key) An arbitrary Taxon identification number.

TaxonCode A code for the Taxon. These codes are useful for other software or output for which the complete nameis too long. Because of the very large number of taxa, codes can be duplicated for different Taxa Groups. Ingeneral, these various Taxa Groups are analyzed separately, and no duplication will occur within a dataset.However, if Taxa Groups are combined, unique codes can be generated by prefixing with the TaxaGroupID, Forexample, VPL:Cle (Clethra) and MAM:Cle (Clethrionomys)

A set of conventions has been established for codes. In some cases conventions differ depending on whether theorganism is covered by rules of botanical nomenclature (BN) or zoological nomenclature (ZN).

Genus Three-letter code, first letter capitalized, generally the first three unless already used: Ace (Acer) or Cle(Clethrionomys).

Subgenus The genus code plus a two-letter subgenus code, first letter capitalized, separated by a period: Pin.Pi(Pinus subg. Pinus) or Syn.Mi (Synaptomys (Mictomys)).

Species The genus code plus a two-letter, lower-case species code, separated by a period: Ace.sa (Acer saccha-rum), Ace.sc (Acer saccharinum), or Cle.ga (Clethrionomys gapperi)



Subspecies or variety The species code a two-letter, lower-case subspecies code, separated by a period:Aln.vi.si (Alnus viridis subsp. sinuata), or Bis.bi.an (Bison bison antiquus)

Family Six-letter code, first letter capitalized, consisting of three letters followed by «eae» (BN) or «dae» (ZN):Roseae (Rosaceae), or Bovdae (Bovidae)

Subfamily or tribe (BN) Family code plus two-letter subfamily code, first letter capitalized, separated by a pe-riod. (ZN) Six-letter code, first letter capitalized, consisting of three letters followed by «nae»: Asteae.As(Asteraceae subf. Asteroideae), Asteae.Cy (Asteraceae tribe Cynarea), or Arvnae Arvicolinae.

Order (BN) Six-letter code, first letter capitalized, consisting of three letters followed by «les». (ZN) Six-lettercode, first letter capitalized, consisting of three letters, followed by the last three letters of the order name,unless the order name is 6 letters long, in which case the code = the order name. Zoological orders do nothave a common ending: Ercles (Ericales), Artyla (Artiodactyla), or Rodtia (Rodentia).

Taxonomic levels higher than order Six-letter code, first letter capitalized, consisting of three letters, fol-lowed by the last three letters of the order name, unless the order name is 6 letters long, in which casethe code = the order name: Magida (Magnoliopsida), Magyta (Magnoliophyta), or Mamlia (Mammalia).

Types The conventional taxon code followed by «-type»: Aln.in-t (Alnus incana-type), Amb-t (Ambrosia-type)

cf. «cf. » is placed in the proper position: Odc.cf.he (Odocoileus cf. O. hemionus), cf.Odc.he (cf. Odocoileushemionus), or cf.Odc (cf. Odocoileus).

aff. «aff. » is abbreviated to «af. »: af.Can.di (aff. Canis dirus)

? «?» is placed in the proper position. ?Pro.lo (?*Procyon lotor*)

Alternative names A slash is placed between the conventional abbreviations for the alternative taxa: Ost/Cpn(Ostrya/Carpinus), or Mstdae/Mepdae (Mustelidae/Mephitidae)

Undifferentiated taxa (BN) «.ud» is added to the code. (ZN) «.sp » is added to the code: Aln.ud (Alnusundiff.), Roseae.ud (Rosaceae undiff.), Mms.sp (Mammuthus sp.), or Taydae.sp (Tayassuidae sp.).

Parenthetic modifiers The conventional taxon code with an appropriate abbreviation for the modifier separatedby periods. Multiple modifiers also separated by periods. Abbreviations for pollen morphological mod-ifiers follow Iversen and Troels-Smith (1950): Raneae.C3 (Ranunculaceae (tricolpate)), Raneae.Cperi(Ranunculaceae (pericolpate)), Pineae.ves.ud (Pinaceae (vesiculate) undiff.), Myteae.Csyn.psi (Myr-taceae (syncolpate, psilate)), Bet.>20µ (Betula (>20 µm))

Non-biological taxa Use appropriate abbreviations: bulk.dens (Bulk density), LOI Loss-on-ignition,Bet.pol.diam (Betula mean pollen-grain diameter).

TaxonName Name of the taxon. Most TaxonNames are biological taxa; however, some are biometric measures andsome are physical parameters. In addition, some biological taxa may have parenthetic non-Latin modifers, e.g.«Betula (>20 µm)» for Betula pollen grains >20 µm in diameter. In general, the names used in Neotoma arethose used by the original investigator. In particular, identifications are not changed, although Dataset notes canbe added to the database regarding particular identifications. However, some corrections and synonymizationsare made.

These include:

• Misspellings are corrected.

• Nomenclatural, homotypic, or objective synonyms may be applied. Because these synonyms unambigu-ously refer to the same taxon, no change in identification is implied. For example, the old family name forthe grasses «Gramineae» is changed to «Poaceae».

• Taxonomic, heterotypic, or subjective synonyms may be applied if the change does not effectively assignthe specimen to a different taxon. Although two names may have been based on different type specimens,if further research has shown that these are in fact the same taxon, the name is changed to the acceptedname. These synonymizations should not cause confusion. However, uncritical synonymization, although



taxonomically correct, can result in loss of information, and should be avoided. For example, although anumber of recent studies have shown that the Taxodiaceae should be merged with the Cupressaceae, sim-ply synonymizing Taxodiaceae with Cupressaceae may expand the universe of taxa beyond that impliedby the original investigator. For example, a palynologist in the southeastern United States may have used«Taxodiaceae» to imply «Taxodium», which is the only genus of the family that has occurred in the regionsince the Pliocene, but used the the family name because, palynologically, Taxodiuim cannot be differ-entiated from other Taxodiaceae. However, well preserved Taxodium pollen grains can be differentiatedfrom the other Cupressaceous genera in the regin, Juniperus and Chamaecyperus. Thus, the appropri-ate synonymization for «Taxodiaceae» in this region would be «Taxodium» or «Taxodium-type», whichwould retain the original taxonomic precision. On the other hand, the old «TCT» shorthand for «Taxodi-aceae/Cupressaceae/Taxaceae» now becomes «Cupressaceae/Taxaceae» with no loss of information.

• For alternative taxonomic desginations, the order may be changed. For example, «Ostrya/Carpinus» wouldbe substituted for «Carpinus/Ostrya».

The database has a number of conventions for uncertainty in identification. The uncertainty is included in thetaxon name. Thus, «Acer pensylvanicum» and «Acer cf. A. pensylvanicum» are two different taxa.

cf.

Latin confer, which means compare. In taxonomy «cf. » generally means that the specimencompares well to or is similar to the type referred, but the identification is uncertain. Uncertaintymay arise for a number of reasons. The specimen may not be well preserved. It may be nonde-script. There may be other similar taxa that can not be ruled out. The analyst may not have accessto a complete reference or comparative collection for the group, so other related taxa cannot beexcluded with certainty.

For uncertainty at the species level, the convention in Neotoma is, for example, «Odocoileus cf. O.hemionus», not «Odocoileus cf. hemionus». Placement of «cf. » is important, because it indicates thetaxonomic level of uncertaintly. For example, «Odocoileus cf. O. hemionus» implies that the identificationof Odocoileus is secure, but that the species identification is not; whereas «cf. Odocoileus hemionus»implies that not even the genus identification is certain. A further implication in the latter example is thatif the genus identification is correct, then the the specimen must also be that species, perhaps because ofbiogeographic considerations. Although commonly overlooked, it is also important to indicate the properlevel of uncertainly in family-genus identifications. For example, «Brassicaceae cf. Brassica» implies thatassignment to the Brassicaceae is secure; whereas simply «cf. Brassica» does not indicate that even thefamily identification is certain.

In FAUNMAP, the uncertainty is recorded in a separate field from the taxon name, and for species it isnot discernable whether the uncertainty is at the genus or species level. When data were imported fromFAUNMAP, the «cf. » uncertainty was conservatively assigned to the genus level. Thus, if «Bison bison»was indicated to have «cf. » uncertainty, this record was imported as «cf. Bison bison» rather than «Bisoncf. B. bison». However, in many cases, the uncertainty in the original data was probably at the specieslevel.

aff. «aff. » Latin affinis, which means having affinity with, but distinct from, the referred taxon. This desgina-tion is often applied to a taxon thought to be undescribed. Thus, «aff. Canis dirus» implies an affinity toCanus dirus, but the specimen is likely from another species.

? «?» is used to designate a questionable identification. It may indicate even less certainty than «cf. ». Anexample is «?Procyon lotor».

Types Many pollen taxa are designated as types, e.g. «Ambrosia-type». A type denotes a morphological typethat is consistent with the referred taxon, but also includes other taxa that are palynologically indistinguish-able. For example, «Ambrosia-type» includes Ambrosia and Iva axillaris. The referred name commonlyindicates the sporophyte taxon thought to be the most probable source of the pollen. An analyst may choosea «-type» designation referring to a lower taxonomic rank rather than an inclusive higher taxonomic rankbecause the referred taxon is thought to be the source taxon with very high probability. For example, in



eastern , Pinus strobus is the only species of Pinus subg. Strobus, although several other species of thissubgenus occur in western . Consequently, some analysts refer to «Pinus strobus-type» rather than «Pinussubg. Strobus». Ideally, a type would comprise a well defined universe of taxa, but in practice types areoften vaguely defined. For example, in eastern «Populus balsamifera-type» includes a large proportionof P. balsamifera and probably smaller proportions of P. tremuloides, P. grandidentata, and P. deltoides;whereas «Populus tremuloides-type» includes larger proportions of these latter three species and a smallerproportion of P. balsamifera. However, these proportions are ill-defined.

Alternative taxonomic designations In some cases, fossil specimens of two taxa are indistinguishable and aremore-or-less equally likely. The names can then be separated by a slash, e.g. «Ostrya/Carpinus», «Mustel-idae/Mephitidae». If one taxon is more likely, the analyst may choose to use a «-type» designation instead,e.g. «Ostrya-type». Although the order of alternative names may be changed by the database, a «-type»designation is not substituted for alternatives. However, the use of more two alternatives is discouraged.In cases in which taxonomic revisions have reduced the number of speices within a taxon, the originaluniverse of species may be retained with the slash designation. An example is «Mustelidae», which inolder literature included the skunks, which have now been placed in their own family the Mephitidae; thus«Mustelidae/Mephitidae» retains the original set of possible taxa.

Undifferentiated taxa Lower taxonomic ranks may not be differentiated. The convention among palynologistsis to specify these by the suffix «undiff. ». Thus, «Rosaceae undiff.» designates undifferentiated Rosaceae.However, palynologists have inconsistently applied the «undiff.» appellation, and the pollen databasesestablished a convention that taxa must be mutually exclusive within a dataset. Thus, if a higher-ranktaxon is present in a dataset, the «undiff.» suffix is applied only if lower-rank taxa are also present. Forexample, if «Spiraea» occurs in a dataset, «Rosaceae» would be changed to «Rosaceae undiff.», becauseSpiraea is a genus in the family Rosaceae. On the other hand, if «Rosaceae undiff.» occurs with no otherRosaceae, then «Rosaceae undiff.» is changed to simply «Rosaceae»; it is implicit that the family is notdifferentiated.

Faunal analysts customarily use the appellation «sp.» to designate undifferentiated taxa. Thus, «Microtus sp.»indicates undifferentiated Microtus. In addition, faunal analysts regularly use the «sp.» designation even whenno lower-rank taxa are identified. The «sp.» appellation is most frequently used with genera. The principle oftaxonomic mutual exclusivity has not been applied to fauanl datasets, although it should probably be considered.

Author Author(s) of the name. Neither the pollen database nor FAUNMAP stored author names, so these do notcurrently exist in Neotoma for plant and mammal names. These databases follow standard taxonomic references(e.g. Flora of North America, Flora Europaea, Wilson and Reeder’s Mammal Species of the World), which, ofcourse, do cite the original authors. However, for beetles, the standard practice is to cite original author names;therefore, this field was added to Neotoma.

HigherTaxonID The TaxonID of the next higher taxonomic rank, for example, the HigherTaxonID for «Bison» isthe TaxonID for «Bovidae». For «cf.’s» and «-types», the next higher rank may be much higher owing to theuncertainty of the identification; the HigherTaxonID for «cf. Bison bison» is the TaxonId for «Mammalia». TheHigherTaxonID implements the taxonomic hierarchy in Neotoma.

Extinct Boolean (True/False) variable. The value is True if the taxon is extinct, False if extant.

TaxaGroupID (Foreign Key) The TaxaGroupID facilitates rapid extraction of taxa groups that are typically groupedtogether for analysis. Some of these groups contain taxa in different classes or phyla. For example, vascu-lar plants include the Spermatophyta and Pteridophyta; the herps include Reptilia and Amphibia; the testateamoebae include taxa from different phyla. Field links to the TaxaGroupTypes table.

PublicationID (Foreign Key) Publication identification number. Field links to the Publications table.

Notes Free form notes or comments about the Taxon.

1.9.7 TaxaGroupTypes

Lookup table for Taxa Group Types. This table is referenced by the Taxa table.



TaxaGroupTypesTaxaGroupID Text PKTaxaGroup Text

TaxaGroupID (Primary Key) A three-letter Taxa Group code.

TaxaGroup The taxa group. Below are some examples:

TaxaGroupID TaxaGroupAVE BirdsBIM Biometric variablesBRY BryophytesBTL Beetles... ...FSH Fish

1.9.8 Variables

This table lists Variables, which always consist of a Taxon and Units of measurement. Variables can also have El-ements, Contexts, and Modifications. Thus, the same taxon with different measurement units (e.g. present/absent,NISP, MNI) are different Variables.

Table: VariablesVariableID Long Integer PKTaxonID Long Integer FK TaxaVariableElementID Long Integer FK VariableElementsVaribleUnitsID Long Integer FK VariableUnitsVariableContextID Long Integer FK VariableContextsVariableModificationID Long Integer FK VariableModifications

VariableID (Primary Key) An arbitrary Variable identification number.

TaxonID (Foreign Key) Taxon identification number. Field links to the Taxa table.

VariableElementID (Foreign Key) Variable Element identification number. Field links to the VariableElementslookup table.

VariableUnitsID (Foreign Key) Variable Units identification number. Field links to the VariableUnits lookup table.

VariableContextID (Foreign Key) Variable Context identification number. Field links to the VariableContextslookup table.

VarialbeModificationID (Foreign Key) Variable Modification identification number. Field links to the Variable-Modifications lookup table.

SQL Example

This query lists the Variables for «Zea mays» with elements and measurement units.

1 SELECT Taxa.TaxonName, VariableElements.VariableElement,2 VariableUnits.VariableUnits3

4 FROM VariableUnits INNER JOIN (VariableElements INNER JOIN (Taxa INNER5 JOIN Variables ON Taxa.TaxonID = Variables.TaxonID) ON6 VariableElements.VariableElementID = Variables.VariableElementID) ON7 VariableUnits.VariableUnitsID = Variables.VariableUnitsID8

9 GROUP BY Taxa.TaxonName, VariableElements.VariableElement,



10 VariableUnits.VariableUnits11

12 HAVING (((Taxa.TaxonName)="Zea mays"));

Result:

TaxonName VariableElement VariableUnitsZea mays cob NISPZea mays glume NISPZea mays kernel NISPZea mays pollen NISPZea mays stalk fiber present/absent

SQL Example

This query lists all sites with Zea mays pollen by designating the VariableElement as «pollen».

1 SELECT Taxa.TaxonName, VariableElements.VariableElement, Sites.SiteName2

3 FROM VariableElements INNER JOIN (Sites INNER JOIN (CollectionUnits4 INNER JOIN (Datasets INNER JOIN (Samples INNER JOIN ((Taxa INNER JOIN5 Variables ON Taxa.TaxonID = Variables.TaxonID) INNER JOIN Data ON6 Variables.VariableID = Data.VariableID) ON Samples.SampleID =7 Data.SampleID) ON Datasets.DatasetID = Samples.DatasetID) ON8 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) ON9 Sites.SiteID = CollectionUnits.SiteID) ON

10 VariableElements.VariableElementID = Variables.VariableElementID11

12 GROUP BY Taxa.TaxonName, VariableElements.VariableElement,13 Sites.SiteName14

15 HAVING (((Taxa.TaxonName)="Zea mays") AND16 ((VariableElements.VariableElement)="pollen"));

The first few lines of the result:

TaxonName VariableElement SiteNameZea mays pollen Almanac PondZea mays pollen BalikhZea mays pollenZea mays pollenZea mays pollen Big John PondZea mays pollen Black PondZea mays pollenZea mays pollen BouaraZea mays pollen

The same result can be obtained by designating the DatasetType as «pollen»:

1 SELECT Taxa.TaxonName, DatasetTypes.DatasetType, Sites.SiteName2

3 FROM DatasetTypes INNER JOIN ((Taxa INNER JOIN Variables ON Taxa.TaxonID4 = Variables.TaxonID) INNER JOIN (Sites INNER JOIN (((CollectionUnits5 INNER JOIN Datasets ON CollectionUnits.CollectionUnitID =6 Datasets.CollectionUnitID) INNER JOIN Samples ON Datasets.DatasetID =7 Samples.DatasetID) INNER JOIN Data ON Samples.SampleID = Data.SampleID)8 ON Sites.SiteID = CollectionUnits.SiteID) ON Variables.VariableID =



9 Data.VariableID) ON DatasetTypes.DatasetTypeID = Datasets.DatasetTypeID10

11 GROUP BY Taxa.TaxonName, DatasetTypes.DatasetType, Sites.SiteName12 HAVING (((Taxa.TaxonName)="Zea mays") AND13 ((DatasetTypes.DatasetType)="pollen"));

SQL Example

This example gives a list of all sites with Bison bison antiquus bones with human butchering.

1 SELECT Taxa.TaxonName, VariableModifications.VariableModification,2 Sites.SiteName3

4 FROM Sites INNER JOIN (CollectionUnits INNER JOIN (Datasets INNER JOIN5 (Samples INNER JOIN ((VariableModifications INNER JOIN (Taxa INNER JOIN6 Variables ON Taxa.TaxonID = Variables.TaxonID) ON7 VariableModifications.VariableModificationID =8 Variables.VariableModificationID) INNER JOIN Data ON9 Variables.VariableID = Data.VariableID) ON Samples.SampleID =

10 Data.SampleID) ON Datasets.DatasetID = Samples.DatasetID) ON11 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) ON12 Sites.SiteID = CollectionUnits.SiteID13

14 GROUP BY Taxa.TaxonName, VariableModifications.VariableModification,15 Sites.SiteName16

17 HAVING (((Taxa.TaxonName)="Bison bison antiquus") AND18 ((VariableModifications.VariableModification)="human butchering"));

Result:

TaxonName VariableModification SiteNameBison bison antiquus human butchering FolsomBison bison antiquus human butchering [41LU1]Bison bison antiquus human butchering Murray Springs [EE:8:25]Bison bison antiquus human butchering San Jon

1.9.9 VariableContexts

Variable Contexts lookup table. Table is referenced by the Variables table.

Table: VariableContextsVariableContextID Long Integer PKVariableContext Text

VariableContextID (Primary Key) An arbitrary Variable Context identification number.

VariableContext Depositional context. Examples are:

anachronic A specimen older than the primary deposit, e.g. a Paleozoic spore in a Holocene deposit. Thespecimen may be redeposited from the catchment, or may be derived from long distance, e.g. Tertiarypollen grains in Quaternary sediments with no local Tertiary source. A Pleistocene specimen in a Holocenearchaeological deposit, possibly resulting from aboriginal fossil collecting, would also be anachronic.

intrusive A specimen, generally younger than the primary deposit, e.g. a domestic pig in an otherwise Pleis-tocene deposit.



redeposited A specimen older than the primary deposit and assumed to have been redeposited from a localsource by natural causes.

articulated An articulated skeleton

clump A clump, esp. of pollen grains

1.9.10 VariableElements

Lookup table of Variable Elements. Table is referenced by the Variables table.

Table: VariableElementsVariableElementID Long Integer PKVariableElement Text

VariableElementID (Primary Key) An arbitrary Variable Element identification number.

VariableElement The element, part, or organ of the taxon identified. For plants, these include pollen, spores, andvarious macrofossil organs, such as «seed», «twig», «cone», and «cone bract». Thus, Betula pollen and Betulaseeds are two different Variables. For mammals, Elements include the bone or tooth identified, e.g. «tibia».«tibia, distal, left», «M2, lower, left». Some more unusual elements are Neotoma fecal pellets and Erethizondorsata quills. If no element is indicated for mammalian fauna, then the genric element «bone/tooth» is as-signed. Elements were not assigned in FAUNMAP, so all Variables ingested from FAUNMAP were assignedthe «bone/tooth» element. Physical Variables may also have elements. For example, the Loss-on-ignition Vari-ables have «Loss-on-ignition» as a Taxon, and temperature of analysis as an element, e.g. «500°C», «900°C».Charcoal Variables have the size fragments as elements, e.g. «75-100 µm», «100-125 µm».

1.9.11 VariableModifications

Lookup table of Variable Modifications. Table is referenced by the Variables table.

Table: VariableModificationsVariableModificationID Long Integer PKVariableModification Text

VariableModificationID (Primary Key) An arbitrary Variable Modification identification number.

VariableModification Modification to a specimen. Examples of modifications to bones include «carnivore gnawed»,«rodent gnawed», «burned», «human butchering». Modifications to pollen grains include various preserva-tion states, e.g. «1/2 grains», «degraded», «corroded», «broken». Most Variables do not have a modificationassigned.

1.9.12 VariableUnits

Lookup table of Variable Units. Table is referenced by the Variables table.

Table: VariableUnitsVariableUnitsID Long Integer PKVariableUnit Text

VariableUnitsID (Primary Key) An arbitrary Variable Units identification number.

VariableUnit The units of measurement. For fauna, these are «present/absent», «NISP» (Number of Individual Spec-imens), and «MNI» (Minimum Number of Individals). For pollen, these are «NISP» (pollen counts) and «per-cent». Units for plant macrofossils include «present/abesnt» and «NISP», as well as a number of quantitativeconcentration measurements and semi-quantitative abundance measurements such as «1-5 scale». Examples ofcharcoal measurement units are «fragments/ml» and «µm^2/ml».



1.9.13 RepositoryInstitutions

A lookup table of institutions that are repositories for fossil specimens. Table is referenced by the RepositorySpecimenstable.

Table: RepositoryInstitutionsRepositoryID Long Integer PKAcronym TextRepository TextNotes Memo

RepositoryID (PrimaryKey) An arbitrary Repository identification number. Repositories include museums, univer-sity departments, and various governmental agencies.

Acronym A unique acronym for the repository. Many repositories have well-established acronyms (e.g. AMNH = ofNatural History); however, there is no official list. Various acronyms have been used for some institutions, andin some cases the same acronym has been used for different institutions. Consequently, the database acronymmay differ from the acronym used in some publications. For example, «CMNH» has been used for the CarnegieMuseum of Natural History, the Cleveland Museum of Natural History, and the Cincinnati Museum of NaturalHistory. In Neotoma, two of these institutions were assigned different acronyms, ones that have been used forthem in other publications: CM – Carnegie Museum of Natural History, CLM – Cleveland Museum of NaturalHistory.

Repository The full name of the repository.

Notes Free form notes or comments about the repository, especially notes about name changes, closures, and specimentransfers. In some cases, it is known that the specimens were transferred, but their current disposition may beuncertain.

1.9.14 RepositorySpecimens

This table lists the repositories in which fossil specimens have been accessioned or reposited. The inventory inNeotoma is by Dataset, which is the collection of specimens from a Collection Unit. Occasionally, specimens froma single Collection Unit have been reposited at different institutions, in which case multiple records for that Datasetoccur in the table.

Table: RepositorySpecimensDatasetID Long Integer PK, FK DatasetsRepositoryID Long Integer PK, FK RepositoryInstitutionsNotes Memo

DatasetID (Primary Key, Foreign Key) Dataset identification number. Field links to the Datasets table.

RepositoryID (Primary Key, Foreign Key) Repository identification number. Field links to the RepositoryInstitu-tions lookup table.

Notes Free form notes or comments about the disposition of the specimens.

SQL Example

This query lists the repositories for specimens from the Kimmswick site.

1 SELECT Sites.SiteName, CollectionUnits.CollUnitName,2 RepositoryInstitutions.Repository3

4 FROM RepositoryInstitutions INNER JOIN (((Sites INNER JOIN5 CollectionUnits ON Sites.SiteID = CollectionUnits.SiteID) INNER JOIN6 Datasets ON CollectionUnits.CollectionUnitID =



7 Datasets.CollectionUnitID) INNER JOIN RepositorySpecimens ON8 Datasets.DatasetID = RepositorySpecimens.DatasetID) ON9 RepositoryInstitutions.RepositoryID = RepositorySpecimens.RepositoryID

10

11 WHERE (((Sites.SiteName)="Kimmswick"));

Result:

SiteName CollUnitName RepositoryKimmswick LocalityKimmswick Locality

1.9.15 SpecimenDates

This table enables queries for dated specimens of indivual taxa. Although the MaterialDated field in the Geochronol-ogy table may list the taxa dated, this protocol is not enforced, and the field is not linked to the taxa table.

Table: SynonymsSpecimenDateID int PKGeochronID int FK GeochronologyTaxonID int FK TaxaVariableElementID int FK VariableElementsSampleID int FK SamplesNotes ntext

SpecimenDateID (Primary Key) An arbitrary specicimen date ID.

GeochronID (Foreign Key) Geochronologic identification number. Field links to the Geochronology table.

TaxonID (Foreign Key) Accepted name in Neotoma. Field links to Taxa table.

VariableElementID (Foreign Key) Variable Element identification number. Field links to the VariableElementslookup table.

SampleID (Primary Key, Foreign Key) Sample ID number. Field links to the Samples table.

Notes Free form notes or comments about dated specimen.

1.10 Publication Related Tables

1.10.1 PublicationAuthors

This table lists authors as their names are given in publications. Only the initials are stored for authors’ given names.The ContactID links to the author’s full name and contact data in the Contacts table. Thus, for a bibliographic entry,Charles Robert Darwin is listed as C. R. Darwin, or as C. Darwin if the publication did not include his middle name.Book editors are also stored in this table if the entire book is cited. However, if a book chapter or section is cited,authors are stored in this table, but the book editors are stored in the PublicationEditors table. Thus, for the followingreference, G. C. Frison is stored in the PublicationAuthors table.

Frison, G. C., editor. 1996. The Mill Iron site. University of New Mexico Press, Albuquerque, NewMexico, USA.

Whereas for the following publication, L. S. Cummings is listed in the PublicationAuthors table, and G. C. Frison islisted in the PublicationEditors table.



Cummings, L. S. 1996. Paleoenvironmental interpretations for the Mill Iron site: stratigraphic pollen andphyrolith analysis. Pages 177-193 in G. C. Frison, editor. The Mill Iron site. University of New MexicoPress, Albuquerque, New Mexico, USA.

Table: PublicationAuthorsAuthorID Long Integer PKPublicationID Long Integer FK PublicationsAuthorOrder Long IntegerFamilyName TextInitials TextSuffix TextContactID Long Integer FK Contacts

AuthorID (Primary Key) An arbitrary Author identification number.


AuthorOrder Ordinal number for the position in which the author’s name appears in the publication’s author list.

FamilyName Family name of author

Initials Initials of author’s given names

Suffix Authors suffix (e.g. «Jr.»)

ContactID (Foreign Key) Contact identification number. Field links to the Contacts table.

SQL Example

The following query lists the PublicationID and complete author names for the publication of . Note that because is aname likely to be duplicated in the database, the name is given with a wild card ending and the GeoPolitical tables arelinked in. The citation for this publication is:

1 SELECT PublicationAuthors.PublicationID, Contacts.ContactName2

3 FROM GeoPoliticalUnits INNER JOIN ((Contacts INNER JOIN ((Publications4 INNER JOIN (((Sites INNER JOIN CollectionUnits ON Sites.SiteID =5 CollectionUnits.SiteID) INNER JOIN Datasets ON6 CollectionUnits.CollectionUnitID = Datasets.CollectionUnitID) INNER JOIN7 DatasetPublications ON Datasets.DatasetID =8 DatasetPublications.DatasetID) ON Publications.PublicationID =9 DatasetPublications.PublicationID) INNER JOIN PublicationAuthors ON

10 Publications.PublicationID = PublicationAuthors.PublicationID) ON11 Contacts.ContactID = PublicationAuthors.ContactID) INNER JOIN12 SiteGeoPolitical ON Sites.SiteID = SiteGeoPolitical.SiteID) ON13 GeoPoliticalUnits.GeoPoliticalID = SiteGeoPolitical.GeoPoliticalID14

15 WHERE (((Sites.SiteName) Like " \*") AND16 ((GeoPoliticalUnits.GeoPoliticalName)=""));

Result:

PublicationID ContactName202 Baker, Richard G.202 Maher, Louis J., Jr.202

12.

202 Chumbley, Craig A.

1.10. Publication Related Tables 67


The citation for PublicationID is:

Baker, R. G., L. J. Maher, Jr., C. A. Chumbley, and K. L. Van Zant. 1992. Patterns of Holocene environmental changein the midwestern United States. Quaternary Research 37:379-389.

1.10.2 PublicationEditors

This table stores the editors of publications for which chapters or sections are the primary bibliographic entries.Chapter authors are stored in the PublicatonAuthors table, where they are linked to the Contacts table. However,publication editors are not cross-referenced in the Contacts table, because chapter authors are the principal citation.

Table: PublicationEditorsEditorID Long Integer PKPublicationID Long Integer FK PublicationsEditorOrder Long IntegerFamilyName TextInitials TextSuffix Text

EditorID (Primary Key) An arbitrary Editor identification number.


EditorOrder Ordinal number for the position in which the editor’s name appears in the publication’s author list.

FamilyName Family name of editor

Initials Initials of editor’s given names

Suffix Authors suffix (e.g. «Jr.»)

1.10.3 Publications

This table stores publication or bibliographic data. The table is designed with fields for bibliographic data so thatbibliographies can be formatted in different styles and potentially exported to bibliographic software such EndNote®.In the constituent databases that were originally merged into Neotoma, bibliographic entries were not parsed intoseparate fields, but rather were stored as free-form text.

Because complete parsing of these thousands of legacy bibliographic entries into individual fields would have beenprohibitively time consuming, the existing bibliographic data were ingested “as is” with a PubTypeID = Other. How-ever, for legacy publications, the year of publication was added to the Year field, and authors were parsed into thePublicationAuthors table and added to the Contacts table. In addition, some global changes were made. For example,«Pp.» was changed to «Pages», «Ed.» to «Editor», and «Eds.» to «Editors». Also for FAUNMAP entries, abbreviatedjournal names were changed to fully spelled out names.

The merged databases used different bibliographic styles, and data entry personnel working on the same databasesometimes followed different conventions. Consequently, the current bibliographic entries are not stylistically uniform.Eventually, the legacy bibliographic data will be parsed into separate fields.

The Publications table has fields to accommodate a number of different types of publications. Some fields containdifferent kinds of data for different kinds of publications. For example, the BookTitle field stores the titles of books,but stores the journal name for journal articles. The Publisher field stores the name of the publisher for books, but thename of the university for theses and dissertations.

Authors are stored in the PublicationAuthors table. Editors are also stored in the PublicationAuthors table if the entirepublication is cited. The PublicationAuthors table has a ContactID field, which links to the Contacts table, where fullnames and contact information is stored for authors and editors. The PubTypeID «Authored Book» or «Edited Book»indicates whether the PublicationAuathors records are authors or editors. If a book chapter or section is the primary



bibliographic entry, then the book editors are stored in the PublicationEditors table, which does not have a ContactIDfield.

Table: PublicationsPublicationID Long Integer PK PublicationsPubTypeID Long Integer FK PublicationTypesYear TextCitation MemoArticleTitle MemoBookTitle TextVolume TextIssue TextPages TextCitationNumber TextDOI TextNumVolumes TextEdition TextVolumeTitle TextSeriesTitle TextSeriesVolume TextPublisher TextCity TextState TextCountry TextOriginalLanguage TextNotes Memo

PublicationID (Primary Key) An arbitrary Publication identification number.

PubTypeID (Foreign Key) Publication type. Field links to the PublicationTypes lookup table.

Year Year of publication.

Citation The complete citation in a standard style. For Legacy citations inherited from other databases, this fieldholds the citation as ingested from the other databases.

ArticleTitle The title of a journal or book chapter article.

BookTitle The title of a book or journal

Volume The volume number of a journal or the volume number of a book in a set. A set of books is comprised of afixed number of volumes and normally have ISBN numbers, not ISSN numbers. Book sets are often publishedsimultaneously, but not necessarily. For instance, many floras, such as The Flora of North America north of andFlora Europaea, consist of a set number of volumes planned in advance but published over a period of years.

Issue Journal issue number, normally included only if issues are independently paginated.

Pages Page numbers for journal or book chapter articles, or the number of pages in theses, dissertations, and reports.

CitationNumber A citation or article number used in lieu of page numbers for digital or online publications, typicallyused in conjunction with the DOI. For example, journals published by the American Geophysical Union since1999 use citation numbers rather than page numbers.

DOI Digital Object Identifier. A unique identifier assigned to digital publications. The DOI consists of a prefixand suffix separated by a slash. The portion before the slash stands for the publisher and is assigned by theInternational DOI Foundation. For example, 10.1029 is the prefix for the American Geophysical Union. Thesuffix is assigned by the publisher according to their protocols. For example, the DOI 10.1029/2002PA000768is for an article submitted to Paleoceanography in 2002 and is article number 768 submitted since the systemwas installed. An example of CitationNumber and DOI:



Barron, J. A., L. Heusser, T. Herbert, and M. Lyle. 2003. High-resolution climaticevolution of coastal northern during the past 16,000 years, Paleoceanography 18(1):1020.DOI:10.1029/2002PA000768.

NumVolumes

Number of volumes in a set of books. Used when the entire set is referenced. An example ofNumVolumes and Edition:

Wilson, D. E., and D. M. Reeder. 2005. Mammal species of the world: a taxonomic and geographic reference.Third edition. 2 volumes. The Johns Hopkins University Press, Baltimore, Maryland, USA.

Edition Edition of a publication.

VolumeTitle

Title of a book volume in a set. Used if the individual volume is referenced. Example of Volume andVolumeTitle:

Flora of North America Editorial Committee. 2002. Flora of North America north of . Volume 26. Magnolio-phyta: Liliidae: Liliales and Orchidales. Oxford University Press, New York, New York, USA.

SeriesTitle

Title of a book series. Book series consist of a series of books, typically published at irregularintervals on sometimes related but different topics. The number of volumes in a series is typicallyopen ended. Book series are often assigned ISSN numbers as well as ISBN numbers. However, incontrast to most serials, book series have individual titles and authors or editors. Citation practicesfor book series vary; sometimes they are cited as books, other times as journals. The default citationfor Neotoma includes all information. An example of SeriesTitle and SeriesVolume:

Curtis, J. H., and D. A. Hodell. 1993. An isotopic and trace element study of ostracods from , : A 10,500 yearrecord of paleosalinity and paleotemperature changes in the . Pages 135-152 in P. K. Swart, K. C. Lohmann, J.McKensie, and S. Savin, editors. Climate change in continental isotopic records. Geophysical Monograph 78.American Geophysical Union, Washington, D.C., USA.

SeriesVolume Volume number in a series.

Publisher Publisher, including commercial publishing houses, university presses, government agencies, and non-governmental organizations, generally the owner of the copyright.

City City in which the publication was published. The first city if a list is given.

State State or province in which the publication was published. Used for the and , not used for many countries.

Country Country in which the publication was published, generally the complete country name, but «» for the .

OriginalLanguage

The original language if the publication or bibliographic citation is translated from another languageor transliterated from a non-Latin character set. Field not needed for non-translated publications inlanguages using the Latin character set. In the following example, the ArticleTitle is translated fromRussian to English and the BookTitle (journal name) is transliterated from Russian:

Tarasov, P.E. 1991. Late Holocene features of the Kokchetav Highland. Vestnik Moskovskogo Universiteta.Series 5. Geography 6:54-60 [in Russian].

Notes Free form notes or comments about the publication, which may be added parenthetically to the citation.

1.10.4 PublicationTypes

Lookup table of Publication Types. This table is referenced by the Publications table.



Table: PublicationTypesPubTypeID Long Integer PKPubType Text

PubTypeID An arbitrary Publication Type identification number.

PubType Publication Type. The database has the following types:

• Legacy Legacy citation ingested from another database and not parsed into separate fields

• Journal Article Article in a journal

• Book Chapter Chapter or section in an edited book

• Authored Book An authored book

• Edited Book An edited book

• Master’s Thesis A Master’s thesis

• Doctoral Dissertation A doctoral dissertation or Ph.D. thesis

• Authored Report An authored report

• Edited Report An edited report

• Other Authored An authored publication not fitting in any other category (e.g. web sites, maps)

• Other Edited A edited publication not fitting into any other category

Examples of the different Publication Types are given in the following sections. Shown for each Publication Type arethe fields in the Publications table that may be filled for that type, with the exception that OriginalLanguage and Notesare not shown unless used.

Legacy Publication

PubTypeID = LegacyAuthors Each author a record in the PublicationAuthors tableYear Year publishedCitation Complete citation as imported

Legacy Example: Imported from the North American Pollen Database

Baker, R. G. 1983. Holocene vegetational history of the western . Pages 109-127 in H.E. Wright, Jr., editor.Late-Quaternary environments of the United States. Volume 2. The Holocene. University of Minnesota Press.Minneapolis, MN.

Au-thors

R G Baker

Year 1979Cita-tion

Baker, R.G. 1983. Holocene vegetational history of the western . Pages 109-127 in H.E. Wright, Jr.,editor. Late-Quaternary environments of the United States. Volume 2. The Holocene. University ofMinnesota Press. Minneapolis, MN.



Legacy Example: Imported from FAUNMAP

Semken, H. A., Jr. 1983. Holocene mammalian biogeography and climatic change in the eastern and centralUnited States. Pp. 182-207 in Late-Quaternary environments of the United States, Volume 2: The Holocene(H. E. Wright, Jr., ed.), University of Minnesota Press, Minneapolis.

Authors8. (a) Semken, Jr.

Year 1983Citation Semken, H. A., Jr. 1983. Holocene mammalian bio-

geography and climatic change in the eastern and centralUnited States. Pp. 182-207 in Late-Quaternary environ-ments of the United States, Volume 2: The Holocene(H. E. Wright, Jr., ed.), University of Minnesota Press,Minneapolis.

Journal Article

PubTypeID = JournalAuthors Each author a record in the PublicationAuthors tableYear Year publishedArticleTitle Article titleBookTitle Journal nameVolume VolumeIssue IssuePages PagesCitationNumber Citation numberDOI Digital Object Identifier

Journal Example: Page numbers

Wright, H.E., Jr., T.C. Winter, and H.L. Patten. 1963. Two pollen diagrams from southeastern : problemsin the regional late-glacial and postglacial vegetational history. Geological Society of Bulletin.74:1371-1396.

Authors H. E. Wright Jr. T. C. Winter H. L. PattenYear 1963ArticleTitle Two pollen diagrams from southeastern : problems in the regional lateglacial and postglacial

vegetational history.BookTitle Geological Society of BulletinVolume 74IssuePages 1371-1396Citation-NumberDOI



Journal Example: Page numbers, article in French, OriginalLanguage not neededRichard, P .J. H. 1979. Contribution à l’histoire postglaciaire de la végétation au nord-est de la Jamésie, Nouveau-Québec. Géographie physique et Quaternaire 33:93-112.Authors Richard, P. J. H.Year 1979ArticleTitle Contribution à l’histoire postglaciaire de la végétation au nord-est de la Jamésie,

Nouveau-QuébecBookTitle Géographie physique et QuaternaireVolume 33IssuePages 93-112CitationNum-berDOI

Journal Example: Citation number and DOIBarron, J. A., L. Heusser, T. Herbert, and M. Lyle. 2003. High-resolution climatic evolution of coastal northern during the past 16,000 years, Paleoceanography 18(1):1020. DOI:10.1029/2002PA000768.

Authors | J. A. BarronL. HeusserT. HerbertM. Lyle

Year 2003ArticleTitle High-resolution climatic evolution of coastal northern

during the past 16,000 yearsBookTitle PaleoceanographyVolume 18Issue 1PagesCitationNumber 1020DOI 10.1029/2002PA000768

Journal Example: Translated and transliterated from RussianTarasov, P.E. 1991. Late Holocene features of the Kokchetav Highland. Vestnik Moskovskogo Universiteta. Series 5. Geography 6:54-60 [in Russian].Authors

16. (a) Tarasov

Year 1991ArticleTitle Late Holocene features of the Kokchetav HighlandBookTitle Vestnik Moskovskogo Universiteta. Series 5. Geogra-

phyVolume 6IssuePages 54-60CitationNumberDOIOriginalLanguage Russian



Book Chapter

PubTypeID = Book ChapterAuthors Each author a record in the PublicationAuthors tableYear Year publishedArticleTitle Article titleEditors Table PublicatonAuthors: AuthorTypeID =EditorVolume Volume in setPages Article pagesBookTitle Book titleEdition EditionVolumeTitle Title of volume in multivolume setSeriesTitle Series titleSeriesVolume Volume in seriesPublisher PublisherCity City where publishedState State or province where publishedCountry Country where published

Book Chapter Example: Volume in a set

Lundelius, E. L., Jr., R. W. Graham, E. Anderson, J. Guilday, J. A. Holman, D. W. Steadman, and S. D. Webb. 1983.Terrestrial vertebrate faunas. Pages 311-353 in S. C. Porter, editor. Late-Quaternary environments of the . Volume 1.The late Pleistocene. of Press, .

Authors E. L. Lundelius Jr. R. W. Graham J. Guilday J. A. Hol-man D. W. Steadman S. D. Webb

Year 1983ArticleTitle Terrestrial vertebrate faunasEditors

19. (a) Porter

Volume 1Pages 311-353BookTitle Late-Quaternary environments of theEditionVolumeTitle The late PleistoceneSeriesTitleSeriesVolumePublisher PressCityStateCountry



Book Chapter Example: Volume in a series

Curtis, J. H., and D. A. Hodell. 1993. An isotopic and trace element study of ostracods from , : A 10,500 year record ofpaleosalinity and paleotemperature changes in the . Pages 135-152 in P. K. Swart, K. C. Lohmann, J. McKensie, and S.Savin, editors. Climate change in continental isotopic records. Geophysical Monograph 78. American Geophysical Union, .

Authors J. H. Curtis D. A. HodellYear 1993

[A 10,500 year record of paleosalinity]

ArticleTitle | An isotopic and trace element study of ostracods from , and paleotemperature changes in the

Editors | P. K. SwartK. C. LohmannJ. McKensieS. Savin

VolumePages 135-152BookTitle Climate change in continental isotopic recordsEditionVolumeTitleSeriesTitle Geophysical MonographSeriesVolume 78Publisher American GeophysicalCityStateCountry



Book Chapter Example: Chapter in an edited technical reportGust, S. 1990. Faunal. Pages 112-113 in J. G. Maniery, editor. Northern Pomo prehistory: archaeological test excavations at CA-MEN 2138, . Technical Report 1. PAR Environmental Services, Inc.,Authors

19. Gust

Year 1990ArticleTitle FaunalEditors

10. (a) Maniery

VolumePages 112-113BookTitle Northern Pomo prehistory: archaeological test excava-

tions at CA-MEN 2138,EditionVolumeTitleSeriesTitle Technical ReportSeriesVolume 71Publisher PAR Environmental Services, Inc.CityStateCountry

Authored Book

A book published by a single, or multiple authors.

PubTypeID = Authored BookAuthors Each author a record in the PublicationAuthors tableYear Year publishedVolume Volume in setBookTitle Book titleEdition EditionNumVolumes Number of volumes in set. Use only if Volume not specified.VolumeTitle Title of volume in multivolume setSeriesTitle Series titleSeriesVolume Series volumePublisher PublisherCity City where publishedState State or province where publishedCountry Country where published



Authored Book ExampleRitchie, J. C. 1987. Postglacial vegetation of . Press, .Authors

10. (a) Ritchie

Year 1987VolumeBookTitle Postglacial vegetation ofEditionNumVolumesVolumeTitleSeriesTitleSeriesVolumePublisher PressCityStateCountry

Authored Book Example: MultivolumeWilson, D. E., and D. M. Reeder. 2005. Mammal species of the world: a taxonomic and geographic reference. Third edition. 2 volumes. The Press, .Authors D. E.Wilson D. M. ReederYear 2005VolumeBookTitle Mammal species of the world: a taxonomic and geographic referenceEdition ThirdNumVolumes 2VolumeTitleSeriesTitleSeriesVolumePublisher The PressCityStateCountry

Authored Book Example: Volume in a setFlora of Editorial Committee. 2002. Flora of North America north of . Volume 26. Magnoliophyta: Liliidae: Liliales and Orchidales. Press, .Authors | Flora of Editorial CommitteeYear | 2002Volume | 26BookTitle | Flora of North America north ofEdition |NumVolumes |VolumeTitle | Magnoliophyta: Liliidae: Liliales and OrchidalesSeriesTitle |SeriesVolume |Publisher | PressCity |State |Country |



Edited Book

PubTypeID = Edited BookAuthors Each editor a record in the PublicationAuthors tableYear Year publishedVolume Volume in setBookTitle Book titleEdition EditionNumVolumes Number of volumes in set. Use only if Volume not specified.VolumeTitle Title of volume in multivolume setSeriesTitle Series titleSeriesVolume Series volumePublisher PublisherCity City where publishedState State or province where publishedCountry Country where published

Edited Book ExampleFrison, G. C., editor. 1996. The Mill Iron site. of Press, .Editors

7. (a) Frison

Year 1996VolumeBookTitle The Mill Iron siteEditionNumVolumesVolumeTitleSeriesTitleSeriesVolumePublisher PressCityStateCountry

Edited Book Example: May also be entered as an edited reportJeter, M. D., editor. 1988. The Burris site and beyond: archeological survey and testing along a pipeline corridor and excavations at a Mississippian village, northeast . Research Report 27. Archeological Survey,Editors

13. (a) Jeter

Year 1988VolumeBookTitle The Burris site and beyond: archeological survey and

testing along a pipeline corridor and excavations at aMississippian village, northeast

EditionNumVolumesVolumeTitleSeriesTitle Research ReportSeriesVolume 27Publisher Archeological SurveyCityStateCountry



Edited Book Example: Second edition of volume in a set

Tutin, T. G., N. A. Burges, A. O. Chater, J. R. Edmondson, V. H. Heywood, D. M. Moore, D. H. Valentine,S. M. Walters, and D. A. Webb, editors. 1993. Flora Europaea. Volume 1. Psilotaceae to Platanaceae. Secondedition. Press, .

Editors T. G. Tutin N. A. Burges A. O. Chater J. R. Edmondson V. H. Heywood D. M. Moore D. H.Valentine S. M. Walters D. A. Webb

YearVolume 1BookTitle Flora EuropaeaEdition SecondNumVol-umesVolumeTi-tle

Psilotaceae to Platanaceae

SeriesTitleSeriesVol-umePublisher PressCityStateCountry

Master’s Thesis

PubTypeID = Master’s ThesisAuthors Author a record in the PublicationAuthors tableYear Year publishedPages Number of pages in thesisBookTitle Title of thesisPublisher University where degree grantedCity City of universityState State or province of universityCountry Country of university

Master’s Thesis ExampleRadle, N. J. 1981. Vegetation history and lake-level changes at a saline lake in northeastern South Dakota. Master’s thesis. University of Minnesota, Minneapolis, Minnesota, USA.Authors

14. (a) Radle

Year 1981Pages 126BookTitle Vegetation history and lake-level changes at a saline lake

in northeastern South Dakota.Publisher University of MinnesotaCity MinneapolisState MinnesotaCountry USA



Doctoral Dissertation

PubTypeID = Doctoral DissertationAuthors Author a record in the PublicationAuthors tableYear Year publishedPages Number of pages in thesisBookTitle Title of thesisPublisher University where degree grantedCity City of universityState State or province of universityCountry Country of university

Doctoral Dissertation ExampleOrtega-Guerrero, B. 1992. Paleomagnetismo, magnetoestratifia y paleoecología del Cuaternario tardío en el Lago de Chalco, Cuenca de México. Doctoral dissertation. Universidad Nacional Autónoma de México, , Mexico.Authors

2. Ortega-Guerrero

Year 1992Pages 161BookTitle Paleomagnetismo, magnetoestratifia y paleoecología del

Cuaternario tardío en el Lago de Chalco, Cuenca deMéxico

Publisher Universidad Nacional Autónoma de MéxicoCityStateCountry

Authored Report

PubTypeID = Authored ReportAuthors Each author a record in the PublicationAuthors tableYear Year publishedPages Number of pages in reportBookTitle Title of reportSeriesTitle Report series or descriptionSeriesVolume Report number if in a numbered seriesPublisher Publisher, may be a government agency or non-governmental organizationCity City where publishedState State where publishedCountry Country where published



Authored Report Example: Numbered seriesHuber, J. K. 2003. Results of a pollen and loss-on-ignition analysis of sediment from a mastodon pulp cavity, North Fork Canoe Creek, . Report 2003-1. James K. Huber Consulting, .Authors

10. (a) Huber

Year 2003PagesBookTitle Results of a pollen and loss-on-ignition analysis of sed-

iment from a mastodon pulp cavity, North Fork CanoeCreek,

SeriesTitle ReportSeriesVolume 2003-1Publisher James K. Huber ConsultingCity VintonStateCountry

Authored Report Example: Numbered series

Daugherty, R. D., J. J. Flenniken, and J. M. Welch. 1987. A data recovery study of rockshelters (45-LE-222)in Lewis County, Washington. United States Department of Agriculture, Forest Service, Region. Studies inCultural Resource Management 8. .

Authors R. D. Daugherty J. J. Flenniken J. M. WelchYear 1987PagesBookTitle A data recovery study of rockshelters (45-LE-222) in Lewis County, WashingtonSeriesTitle Studies in Cultural Resource ManagementSeriesVolume 8Publisher United States Department of Agriculture, Forest Service, RegionCityStateCountry



Authored Report Example: Report series is a description

Cannon, K. P. 1997. The analysis of a late Holocene bison skull from Fawn Creek, , and its implications forunderstanding the history and ecology of bison in the Intermountain West. Report prepared for The Departmentof Agriculture, United States Forest Service, Salmon-Challis National Forest, Salmon, Idaho. United StatesDepartment of Interior, National Park Service, Midwest Archeological Center, .

Authors11. (a) Cannon

Year 1997PagesBookTitle The analysis of a late Holocene bison skull from Fawn

Creek, , and its implications for understanding the his-tory and ecology of bison in the Intermountain West

SeriesTitle Report prepared for The Department of Agriculture,United States Forest Service, Salmon-Challis NationalForest, Salmon, Idaho

SeriesVolumePublisher United States Department of Interior, National Park Ser-

vice, Archeological CenterCityStateCountry

Edited Report

PubTypeID = Edited ReportAuthors Each editor a record in the PublicationAuthors tableYear Year publishedPages Number of pages in reportBookTitle Title of reportSeriesTitle Report series or descriptionSeriesVolume Report number if in a numbered seriesPublisher Publisher, may be a government agency or non-governmental organizationCity City where publishedState State where publishedCountry Country where published

Edited Report Example: Numbered seriesFishel, R. L., editor. 1999. Bison hunters of the western prairies: archaeological investigations at the site (13WD8), . Report 21. Office of the State Archaeologist, University of , .Authors

18. (a) Fishel

Year 1999PagesBookTitle Bison hunters of the western prairies: archaeological in-

vestigations at the site (13WD8).SeriesTitle ReportSeriesVolume 21Publisher Office of the State Archaeologist,CityStateCountry



Other Authored Publication

PubTypeID = Other AuthoredAuthors Each author a record in the PublicationAuthors tableYear Year publishedArticleTitle Rest of citation

Other Example: Web siteStevens, P.F. 2007. Angiosperm Phylogeny Website. Version 8, June 2007. http://www.mobot.org/MOBOT/research/APweb/Authors

16. (a) Stevens

Year 2007ArticleTitle Angiosperm Phylogeny Website. Version 8, June 2007.

http://www.mobot.org/MOBOT/research/APweb/

Other Edited Publication

PubTypeID = Other EditedAuthors Each editor a record in the PublicationAuthors tableYear Year publishedArticleTitle Rest of citation

1.11 Contact and Individual Related Tables

1.11.1 Collectors

The Collectors table lists the people who collected Collection Units.

CollectorsCollectorID Long Integer PKCollectionUnitID Long Integer FK CollectionUnitsContactID Long Integer FK ContactsCollectorOrder Long Integer

CollectorID (Primary Key) An arbitrary Collector identification number.

CollectionUnitID (Foreign Key) CollectionUnit collected. Field links to CollectionUnits table.

ContactID (Foreign Key) Person who collected the CollectionUnit. Multiple individuals are listed in separaterecords. Field links to the Contacts table.

CollectorOrder Order in which Collectors should be listed.

1.11.2 Contacts

This table lists persons and organizations. The table is referenced through Foreign Keys in the following ta-bles: Chronologies, Collectors, DatasetPIs, DatasetSubmissions, Projects, PublicationAuthors, SampleAnalysts, andSiteImages tables.

1.11. Contact and Individual Related Tables 83




ContactsContactID Long Integer PKAliasID Long Integer FK Contacts:ContactIDContactName TextContactStatusID Long Integer FK ContactStatusesFamilyName TextLeadingInitials TextGivenNames TextSuffix TextTitle TextPhone TextFax TextEmail TextURL TextAddress MemoNotes Memo

ContactID (Primary Key) An arbitrary Contact identification number.

AliasID (Foreign Key) The ContactID of a person’s current name. If the AliasID is different from the ContactID,the ContactID refers to the person’s former name. For example, if J. L. Bouvier became J. B. Kennedy, theContactID for J. B. Kennedy is the AliasID for J. L. Bouvier.

ContactName Full name of the person, last name first (e.g. «Simpson, George Gaylord») or name of organization orproject (e.g. «Great Plains Flora Association»).

ContactStatusID (Foreign Key) Current status of the person, organization, or project. Field links to the ContactSta-tuses lookup table.

FamilyName Family or surname name of a person.

LeadingInitials Leading initials for given or forenames without spaces (e.g. «G.G.»).

GivenNames Given or forenames of a person (e.g. «George Gaylord»). Initials with spaces are used if full givennames are not known (e.g. «G. G»).

Suffix Suffix of a person’s name (e.g. «Jr.», «III»).

Title A person’s title (e.g. «Dr.», «Prof.», «»).

Phone Telephone number.

Fax Fax number.

Email Email address.

URL Universal Resource Locator, an Internet World Wide Web address.

Address Full mailing address.

Notes Free form notes or comments about the person, organization, or project.

1.11.3 ContactStatuses

Lookup table of Contact Statuses. Table is referenced by the Contacts table.

ContactStatusesContactStatusID Long Integer PKContactStatus TextStatusDescription Text



ContactStatusID (Primary Key) An arbitrary Contact Status identification number.

ContactStatus Status of person, organization, or project.

StatusDescription

Description of the status. The following statuses exist (with descriptions):

• active Person, project, or organization is active in the field

• deceased Person is deceased

• defunct Project or organization is defunct or non-operational

• extant Project or organization is extant

• inactive Person is inactive in the field

• retired Person is retired

• unknown Status is unknown

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neotoma Documentation€¦ · CHAPTER 1 Acknowledgements This documentation would not be possible without the extrordinary work of Dr. Eric C. Grimm who has spent countless hours